text:
Multi-view image fusion improves resolution in three-dimensional microscopy
Jim Swoger, Peter Verveer, Klaus Greger, Jan Huisken, and Ernst H.K. Stelzer
Jim Swoger,1,* Peter Verveer,2 Klaus Greger, Jan Huisken,3 and Ernst H.K. Stelzer
1Cell Biology & Biophysics Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, D-69117 Heidelberg, Germany.
1Current affiliation: Systems Biology Programme, Centre for Genomic Regulation, C/ Dr. Aiguader 88, 08003 Barcelona, Spain.
2Current affiliation: Department of Systemic Cell Biology, Max Plank Institute of Molecular Physiology, Otto-Hahn-Strasse 11, 44227, Dortmund, Germany.
3Current affiliation: Dept. of Biochemistry & Biophysics, University of California, San Francisco, 1550 Fourth St., San Francisco, CA 94143-2711, U.S.A.
*Corresponding author: jim.swoger@crg.es
J Swoger
P Verveer
K Greger
J Huisken
E Stelzer
Jim Swoger, Peter Verveer, Klaus Greger, Jan Huisken, and Ernst H.K. Stelzer, "Multi-view image fusion improves resolution in three-dimensional microscopy," Opt. Express 15, 8029-8042 (2007)
Two plus one is almost three: a fast approximation for multi-view deconvolution
Manuel Hüpfel, et al.
Biomed. Opt. Express 13(1) 147-158 (2022)
Deep learning-enabled efficient image restoration for 3D microscopy of turbid biological specimens
Le Xiao, et al.
Three-dimensional laser microsurgery in light-sheet based microscopy (SPIM)
Christoph J. Engelbrecht, et al.
Opt. Express 15(10) 6420-6430 (2007)
Image processing algorithms
Three dimensional microscopy
X ray computed tomography
Image processing (100.0100)
Image reconstruction techniques (100.3010)
Three-dimensional image processing (100.6890)
Three-dimensional microscopy (180.6900)
Original Manuscript: March 26, 2007
Revised Manuscript: May 23, 2007
Virtual Journal for Biomedical Optics Vol. 2, Iss. 7
Suppl. Mat. (4)
A non-blind, shift-invariant image processing technique that fuses multi-view three-dimensional image data sets into a single, high quality three-dimensional image is presented. It is effective for 1) improving the resolution and isotropy in images of transparent specimens, and 2) improving the uniformity of the image quality of partially opaque samples. This is demonstrated with fluorescent samples such as Drosophila melanogaster and Medaka embryos and pollen grains imaged by Selective Plane Illumination Microscopy (SPIM). The application of the algorithm to SPIM data yields high-resolution images of organ structure and gene expression, in some cases at a sub-cellular level, throughout specimens ranging from several microns up to a millimeter in size.
©2007 Optical Society of America
1.1. Techniques for high-resolution optical imaging in biology
A serious problem that is encountered in traditional optical microscopy is the fact that the axial resolution in a three-dimensional (3D) image stack is not as good as the lateral resolution. This is a limitation for modern biological applications, where the axial resolution (typically > ~1 μm) is insufficient to resolve sub-cellular phenomena. Several novel types of instrumentation, such as confocal 4Pi [1] and theta [2] microscopies, optical coherence tomography (OCT) [3], I5M [4], and stimulated emission depletion (STED) microscopy [5], have been proposed to overcome this problem. Although these techniques are effective in the applications for which they were designed, they are all technologically challenging. Apart from theta microscopy, all are based around hardware solutions involving coherent optics, which places strict requirements on the hardware's precision and stability.
Another approach which does not require coherent optics is based on the extension of X-ray computed tomography (CT) techniques into the visible region of the spectrum. Although the details vary, all such techniques involve combining information obtained from multiple views of the sample. Initial implementations involved computation of multiple projections from 3D optical image stacks [6,7], which were then combined by traditional CT processing. More recently optical projection tomography (OPT) [8], in which the projections are generated directly by the optical system, has been shown to be a valuable tool for developmental biology [9].
Unlike in X-ray CT, in which all images are projections, in optical microscopy it is possible to generate 3D optical image stacks from multiple viewpoints. Such multi-view stacks have been generated by both traditional wide-field (i.e. non-sectioned) [10–13] and confocal [14] methods. Although the processing required to do tomography with true 3D data stacks is somewhat different from classical CT (see Section 1.3, below), such techniques generally require fewer views of the sample to achieve optimal image quality.
A generalization of the idea of multi-view image fusion is given in Verveer & Jovin [15], in which images recorded with different modes of microscopy (in their case, confocal and wide-field), rather than different viewing directions, are combined. This method of multi-mode image fusion is equally applicable to multi-view image fusion, as is demonstrated using the MVD-MAPGG algorithm in the Results section, below.
Light sheet based fluorescence microscopy techniques, including such variants as Selective (or Single) Plane Illumination Microscopy (SPIM) [16], Orthogonal Plane Fluorescence Optical Sectioning (OPFOS) [17], and ultramicroscopy [18], make it possible to generate 3D optically sectioned fluorescence images of biological samples. If, as is the case with SPIM, multiple 3D stacks are acquired from different viewing angles, this data is particularly well suited to further enhancement by multi-view image fusion. This is because it provides quantitative 3D fluorescence data with a high signal-to-noise ration (SNR) that permits the use of non-linear reconstruction algorithms. Recently we presented some results from a SPIM system, including our initial multi-view reconstructions [16,19]. In this work we describe the details of and improvements to the image processing algorithms used to combine data from multiple viewing directions, and demonstrate their effectiveness on several biological SPIM data sets.
A schematic of the SPIM setup is shown in Fig. 1. The detection arm (OL, EM, TL, and CCD) constitutes a traditional wide-field fluorescence microscope. Illumination is achieved through a cylindrical lens (CL), which forms a thin light sheet whose centre coincides with the focal plane of the detection optics inside the sample (S). This configuration provides optical sectioning and reduced photo-damage to the sample, because only the plane on which the CCD camera is focused is illuminated. The sample is typically mounted in a cylinder of agarose gel immersed in an appropriate aqueous medium. This allows living samples to be maintained in a viable environment while minimizing aberrations due to refractive index mismatches. The gel containing the sample is attached to a stage that translates the sample along the detection axis, so that a 3D image stack of the sample can be recorded. It also allows rotation of the sample around the vertical axis (perpendicular to both the illumination and detection axes), so that multiple 3D stacks can be generated along different viewing angles. Further details of the SPIM hardware can be found in Greger et al [20].
1.2. Multi-view image processing goals
For many applications (e.g. thin cells cultured on a flat cover slip) single-view images of the sample are adequate. However, in recent years there has been a growing interest in quantitative 3D imaging of thicker specimens [21], and such imaging presents challenges that are not issues in traditional 2D microscopy.
Fig. 1. SPIM schematic. The sample (S) is illuminated by a thin light sheet generated by passing a collimated laser beam through a cylindrical lens (CL). The region of the sample that is imaged onto the CCD camera by the objective (OL) and tube (TL) lenses is illuminated by the light sheet. The emission filter (EM) blocks scattered illumination light. The sample is scanned along the detection axis to create a 3D image stack. It can then be physically rotated to different orientations and re-scanned, generating sets of 3D stacks along different viewing angles. An immersion-medium-filled chamber (IC) encloses the specimen and reduces the negative effects of aberration-inducing interfaces.
For simplicity we restrict ourselves in what follows to a discussion of fluorescent samples, although similar arguments could be made for other contrast mechanisms.
There are two main categories into which we divide biological samples: (nearly) transparent and (partially) opaque. By transparent samples we mean those with relatively low scattering/absorption, so that the image resolution does not vary significantly with depth in the sample. For the sample fluorophore distribution sketched in Fig. 2(a), the images detected along two perpendicular axes are shown in Fig. 2(b) and (c). In such cases image distortions are primarily caused by the optical system, i.e. the image is blurred by the point-spread function (PSF) of the microscope. These images have an anisotropic resolution (most optical microscopes have poorer resolution axially than laterally), but the resolution is spatially invariant (the amount of blurring does not depend on the location within the sample) [22]. Since each view has its best resolution along a different direction (e.g. vertical and horizontal in Figs. 2(b) & (c), respectively), the object of the image fusion is to achieve isotropic resolution by combining the best information from each view (as in Fig. 2(d)).
The case of a partially opaque sample is illustrated in the bottom row of Fig. 2. Here the primary problem is not anisotropic resolution, but that the image quality is degraded as one images deeper inside the sample. In this sort of sample, the resolution will also be anisotropic (when imaged with a traditional single-objective microscope), but when the absorption is high this anisotropy is often a relatively minor effect. Multi-view imaging allows us to observe different regions of the sample (Figs. 2(f) and (g)). In this case the goal of multi-view fusion is to combine the information about the different regions of the sample, as illustrated in Fig. 2(h).
These two categories of samples are extreme cases: many samples of practical interest lie somewhere in between and present characteristics of both types. The fusion process described in this work can be beneficial for them in both of these respects. It should be noted that previous work in multi-view microscopy has concentrated primarily on resolution improvement in relatively transparent samples (e.g. Sätzler & Eils [23], Kikuchi et al. [24]). Two useful properties of the multi-view fusion algorithm described in this work are therefore that it is robust enough to handle samples that lie at or anywhere between these two extremes, and that the amount or degree of each type of degradation need not be specified.
Fig. 2. Illustration of image distortions caused by anisotropic and spatially varying image quality, and the compensation thereof by fusing two images. a) The undistorted sample fluorophore distribution. Top row: Images distorted by reduced resolution horizontally (b) or vertically (c). d) Fusion of b) and c) shows improved resolution isotropy. Bottom row: Images distorted by absorption while detecting from the left (f) and from the right (g). h) Fusion of f) and g) shows improved spatial coverage of the sample. Arrows indicate the directions along which the light is detected.
2.1 Transparent samples
2.1.1 Types of image fusion
The first example of multi-view fusion that we present, an example of a (mostly) transparent specimen, uses images of a grain of paper mulberry pollen. Thirty-six 3D stacks with equal angular spacing were taken of the autofluorescence intensity distribution of the pollen grain. From one of these 36 views we extracted optical slices through the middle of the pollen grain, in a plane parallel to both the illumination & detection axes (Fig. 3(a)). Despite the optical sectioning provided by the SPIM light sheet illumination, the inferior resolution along the detection axis is evident. Figure 3(b) shows the corresponding slice through a data set taken in an orientation perpendicular to that in Fig. 3(a), in which the inferior resolution axis is orthogonal to that in Fig. 3(a).
The arithmetic-mean fusion of the 36 views is shown in Fig. 3(c). The resolution is clearly more isotropic than in the single views, and the fact that the grain consists of a spherical shell (the sporoderm) surrounding an inner core becomes apparent. However, fine details of the structure are still obscured. Figure 3(d) shows the result of image fusion by weighted spectral averaging (similar to the Fourier reconstruction used in CT [25]). The contrast and sharpness of the slices is slightly better than that obtained by the arithmetic mean, which is a result of the preferential selection of high-signal components in the weighted spectral fusion.
The results of the multi-view deconvolution (MVD) fusions using iterative Wiener (MVD-Wiener) and maximum a posteriori with Gaussian noise and prior [15] (MVD-MAPGG) methods (see Methods for details) are shown in Figs. 3(e) and (f), respectively. Both of these methods make use of the PSF to correct for the response of the optical system, and both are a significant improvement over either of the non-iterative fusions: the structures in the interior are much better resolved, and it is clear that the grain is encased in a continuous thin shell. The results of the two iterative fusions are very similar, although the MVD-Wiener does tend to exhibit slightly higher background and ringing artifacts.
Fig. 3. Slices perpendicular to the rotation axis, through 3D auto fluorescence data stacks of a paper mulberry pollen grain. a) 0°-view. b) 90°-view. c-f) Fusions of 36 views by: c) arithmetic mean; d) weighted spectral mean; e) MVD-Wiener; f) MVD-MAPGG. Arrowheads in a) and b) indicate the directions of the respective detection axes. Scale bar = 3 μm. See also Movie 1 (1.55MB), slices rotating through the grain of paper mulberry pollen, for which the left image is the 0° view, and the right is the MVD-MAPGG fusion. [Media 1]
Overall, there is a clear progression from the raw data sets to the non-iterative fusions to the MVD fusions. This is a result of the increased information input that is involved in the fusions: those that do not involve deconvolution are improved over the raw data because they combine information from different (anisotropic) views. In addition to this, the MVD fusions also make use of the PSFs, which contain information about the optical system used to generate the raw images.
2.1.2 Effects of the number of views fused
One question that arises when doing multi-view fusions is: What is the optimal number of views to combine? A large number of views will clearly contain more (possibly redundant) information about the sample, but will also require extended data acquisition and processing times, and can result in increased photo-damage to the sample. We illustrate the effect of the number of views on the fusion results in Fig. 4, where MVD-MAPGG fusions have been done for sets of 1, 2, 4, 6, 12, and 18 views of a pollen grain similar to that used in Fig. 3. As can be seen by comparing Figs. 4(a) & (b), combining views along the same axis does not improve the image isotropy, although two anti-parallel views are not identical because even the small pollen grain is not completely transparent. As the number of views is increased, (Figs. 4(c)–(f)), the anisotropy-related artifacts in the images are reduced. This effect can be observed in the smoothness of the shell of the pollen grain in the top row, as the number of views increases. It is also evident in the increased radial symmetry of the corresponding spectral plots, where the "gaps" between the "spokes" in the spectra are reduced as more views are included (the "gaps" are regions in which the reconstruction contains little or no information about the sample).
For this sample, a combination of 18 views (with consecutive views separated by 20°) provides isotropic resolution, and the inclusion of more views does not significantly improve the image quality. This data set was measured using SPIM with a water-immersion objective lens with a numerical aperture (NA) of 0.9, which has a detection half-angle of α = 42.6° (where NA = ni∙sin(α), ni = 1.33 is the refractive index of water). It is quite difficult in general to estimate a priori the optimal number of views to process, because this number will depend not only on the configuration of the instrument that acquires the data (e.g. the NA of the objective lens, and, for a SPIM system the light sheet thickness), but also on the sample properties. The amount of optical attenuation (i.e. the scattering and/or absorption properties) and the physical structure of the sample will affect which regions will be well imaged from a given viewing direction, and therefore the optimal number and arrangement of the views. However, we have found that for mostly transparent samples such as the pollen grain in Fig. 4, as a rule of thumb the optimal number of views can be estimated using Noptimal ~ 360°/(α/2), which results in Noptimal ~ 17 for the specimen shown in Fig. 4.
Fig. 4. Effects of the number of views used in the MVD-MAPGG fusion of paper mulberry pollen autofluorescence images. Top: slices perpendicular to SPIM rotation axis, for different numbers of fused images. Bottom: corresponding power spectra (plotted with a non-linear look-up table to emphasize the high-frequency components). The detection axes are indicated by the white arrows in a-d. Scale bar = 3 μm.
2.2 Opaque samples
2.2.1 Sub-cellular resolution over an entire embryo
In addition to nearly transparent samples such as the pollen grain shown in the previous section, multi-view image fusion is useful for improving data sets from thick, partially opaque specimens [16]. A Drosophila melanogaster embryo, which is difficult to image in its entirety because of its highly scattering tissue, is shown in Fig. 5. The nuclear proteins BJ1 [26] (green) and Chriz [27] (red) are visualized. Figure 5(a) shows projections through a single-view data stack, in which less than a quarter of the specimen is visible. In contrast, the entire embryo is clearly visible in the 8-view MVD-Wiener fusion shown in Fig. 5(b). Figure 5(c) shows the sub-region of the fused data set indicated in Fig. 5(b), which clearly shows the individual nuclei, and the separate domains of the two labeled proteins. Even though a low power objective lens (M = 10×, NA = 0.3 water immersion) was used to acquire the raw data, the multi-view fusion yields sub-cellular resolution over the entire labeled outer region of the embryo.
2.2.2 Imaging deep within an embryo
Figure 5 demonstrates whole-embryo multi-view image fusion. However, because of the sample properties only the outer layers contain visible fluorescent structures. The sample therefore does not give a clear indication of the 3D depth penetration achievable with multi-view SPIM data. To demonstrate that this technique can provide improved resolution within a thick biological specimen, we show images of a stage 32 Medaka fish embryo with a general nuclear staining (green) and in situ hybridization for the gene McF0001MGR-1G19bd1 (red) [28]. Figures 6(a) and (b) depict maximum-value projections along two orthogonal axes of the embryo. Images in the left column are from a single-view data set; those on the right are from a 6-view MVD-MAPGG fusion. The improvements in sharpness, contrast, and uniformity of the fused images are apparent. The inset shows a traditional wide-field
Fig. 5. Drosophila melanogaster embryo. Green: uncondensed DNA; red: condensed DNA. a) Maximum-value projections of a single-view data set, through the ventral (top) and dorsal (bottom) halves of the embryo. b) as a), but for an 8-view MVD-Wiener fusion. c) Expanded image of the 100 μm wide region indicated in b). Scale bar = 100 μm. See also Movie 2, maximum-value projections through the MVD-Wiener fusion of the Drosophila embryo. [Media 2]
transmission image, in which the in situ localization is in agreement with that seen in the SPIM images: McF0001MGR-1G19bd1 is primarily expressed in the tectum proliferation zone, the pineal gland, and the telencephalon.
To provide a better visualization of the 3D imaging capability of multi-view image fusion, Fig. 6(c) shows a slice through the head of the embryo at the position indicated by the dashed line in Fig. 6(a). Much of the detail in the single-view slice (left) is obscured by blurring and absorption. In contrast, the fusion of six views (right) shows considerable detail throughout the cross-section of the embryo. Various layers in the eye can be easily identified, as well as the structures of other tissues within the head of the embryo. (Note that the intensity of the red channel in Fig. 6(c) has been increased relative to that in Figs. 6(a) and (b) to make the expression of McF0001MGR-1G19bd1 in the forebrain and the lens of the eye apparent.)
In the single-view images dark stripes can be seen running horizontally through the embryo in Fig. 6. These are shadows caused by strong absorption of the illumination light, which can occur in all types of fluorescence microscopy. However, because of the collimated nature of SPIM illumination these artifacts are more obvious, and can extend through the entire sample. This can make quantification of fluorophore distributions from single-view SPIM data sets difficult, if the sample exhibits significant amounts of pigmentation. However, in addition to the overall improvement in image resolution and uniformity provided by the six-view MVD-MAPGG fusion, this data also demonstrates that multi-view fusion of SPIM data sets effectively reduces shadowing artifacts.
Fig. 6. Medaka embryo, stage 32, nuclear label (green) and McF0001MGR-1G19bd1 in situ hybridization (red). Left: single-view images; right: 6-view MAPGG fusion. a,b) Maximum-value projections along orthogonal axes. The regions of expression of McF0001MGR-1G19bd1 in the tectum proliferative zone (TPZ), pineal gland (PG), and telencephalon (tel) are clearly visible. For comparison, the inset shows the traditional blue-labeled transmission image. c) Slice at the depth indicated by the dashed line in a). Internal structures such as the lens (L), pigmented epithelium (PE), ganglion cell layer (GCL), outer (ONL) and inner nuclear layers (INL), and the inner plexiform layer (IPL) of the retina are well-defined in the fusion. The illumination (ill), detection (det), and rotation (rot) axes are indicated for the single-view images. Scale bar = 200 μm. See also Movie 3, an animation of the MVD-MAPGG fusion showing maximum-value projections at the top and slices at the position indicated by the white line at the bottom. [Media 3]
3.1. Sample preparation
Pollen: Paper mulberry pollen grains (Polysciences Inc., Warrington, PA, USA) were used as supplied.
Drosophila: Fixed blastocyte samples were labeled for the Drosophila proteins BJ1 [27] (FITC labeled GAM Ig(H+L), Jackson Imm. Res.; monoclonal mouse antibody) and Chriz [28] (TRITC labeled GAR Ig(H+L), Jackson Imm. Res.; rabbit polyclonal antibody).
Medaka: Whole-mount in situ hybridization was performed using digoxigenin-labeled RNA riboprobes as described previously in Quiring et al [29]. After in situ hybridization, embryos were incubated 10 minutes in 1 mM Sytox green (diluted in 1 M Tris, pH 7.5) and washed three times with 1 M Tris, pH 7.5.
All samples were mounted in 1% (or slightly lower) low melting agarose gel (Sigma-Aldrich, St. Louis, MO, USA) prepared using a phosphate buffered saline (PBS) solution.
3.2. Image acquisition
The details of the SPIM are given in [16,20], and sketched in Fig. 1. Briefly, after mounting in the agarose gel the specimens were attached to the translation stage of the SPIM and stacks along different viewing directions were recorded sequentially. Detection was performed using water dipping microscope objective lenses ranging from 63×/0.9 for the pollen (λill = 488 nm, λdet >510nm) to 10×/0.3 for the Drosophila embryo (λill,1 = 488 nm, λdet,1 > 510 nm, λill,2 = 543 nm, 570 nm < λdet2 <650 nm). The exception was the Medaka embryo, which was imaged using a 5×/0.25 air objective (λill,1 = 488 nm, λdet1 > 510 nm, λill,2 = 543 nm, 570 nm < λdet,2 <650 nm). The camera used was a 12-bit Hamamatsu Orca-ER with a pixel pitch of 6.45 μm. The technical details of the optimization of the illumination and detection systems are given in Engelbrecht [30]; as described therein, for all imaging in the present work, the SPIM illumination light sheet was configured so as to illuminate the field of view of the CCD camera with ≤ 1 - 2-0.5 ~ 29% variation in intensity.
The deconvolution methods used in this work are non-blind; i.e. they require as inputs the PSF of each view used. These were generated by imaging sub-resolution fluorescent beads by SPIM under the same conditions used for the samples to be deconvolved. Several (6-10) beads were selected, registered, and averaged to reduce noise, producing an estimate of the system PSF. To generate the appropriate PSF for each view, multiple copies of the original system PSF are made and pre-processed as outlined in Section 3.3.1, below.
3.3. Image processing
In general the image processing algorithm used in this work can be divided into three main steps: pre-processing (consisting of axial re-sampling and stack rotation), registration of the individual views, and the fusion of the registered views into a single description of the sample. The details of the algorithm used for processing 3D multi-view data sets are given in Fig. 7. The technique is an adaptation (to the data SPIM data sets generated by rotating the sample around the vertical axis) and extension (by including the MVD options) of that described in Swoger et al. [31] for the four-view fusion of stacks acquired with a specific (tetrahedral) geometry.
3.3.1 Pre-processing
The purpose of pre-processing the image data is to transform the data sets so that the subsequent step of registration is simplified. Proper pre-processing of SPIM data, for example, makes it possible to register the images by a 3D translation, rather than requiring a 12-parameter affine transformation. The pre-processing transformation is based on known parameters of the imaging system (the pixel pitch, optical magnification, angle of rotation between the views, and the spacing of the slices in the stacks), rather than information obtained from the images themselves. As illustrated in Fig. 7, each stack is rescaled axially by bicubic interpolation so that the axial slice spacing is equal to the lateral pixel spacing. After this the 3D stacks are rotated (again, using bicubic interpolation) so that they share a common orientation (generally, but not necessarily, the orientation of the first stack).
3.3.2 Registration
Image registration is the process of aligning multiple images so that the common features overlap. [32] There are two main questions that need to be answered when choosing a registration method: 1) what type of transformation is required to perform the registration, and 2) how are the parameters for that transformation to be determined. Transformations ranging from simple translations to higher order global or local transformations can be applied, depending on the properties of the data sets. In general the higher-order transformations can correct for more complicated types of mis-registration (e.g. resulting from inaccuracies in the rotation stage or in the calibration of the image scaling, or those due to optical aberrations in the imaging system), but they are difficult to automate and computationally time-consuming, and are therefore to be avoided if possible.
Fig. 7. Image processing algorithm. Inputs: N, number of views; ij, j th image; ϕj, j th viewing angle; M, optical magnification; ∆x, pixel pitch (lateral); ∆z, slice spacing (axial); f HP, high-pass spatial frequency filter; r max, maximum registration error; pj, j th PSF; μ, MVD-Wiener regularization parameter; k max, MVD-Wiener termination parameter; g σ, MVD-MAPGG Gaussian filter; k max, MVD-MAPGG maximum number of iterations. Output: e, sample distribution estimate. Internal: r⃑ = (x,y,z), position; s⃑, spatial frequency; I⃑ , mean image integral; e 0, registration target; xj, j th cross-correlation; r⃑, position of peak of xj; r, gradient vector; d, search direction; α, step size along d. Functional: Σwj , weighted average (see text); CGA, conjugate gradient algorithm [3]; MAPGG, maximum a posteriori with Gaussian noise and prior algorithm [15]; *, convolution operator; ⊗, correlation operator; ã , Fourier transform of a.
The image registration employed in this work is purely translational, which is advantageous because it is a relatively simple transformation. The shift vectors required for the translations are found by cross-correlation, implemented using fast Fourier transform algorithms. In principle these shift vectors might be extracted directly from the parameters sent to the scanning hardware during image acquisition, but this option was not included in our implementation of the algorithm.
We have found that in some samples (the "mostly opaque" ones), registration of all of the views to a fixed target often fails because some views have virtually no overlap with the target. To overcome this difficulty, in the initial stage of registration we have implemented a "running sum" as the target; e.g. if one has views at 0°, 90°, and 180°, the 0° is used as the target to which the 90° view is registered, and then the sum of the 0° and (registered) 90° views is used as the target for the 180° registration. The spatially non-uniform image quality in the single view of the embryo in Fig. 5(a) is apparent. Figure 8 shows cross-sections from the same data set, which illustrate the need for the running-mean correlation target described above.
Fig. 8. Slices perpendicular to the anterior/posterior axis of the Drosophila melanogaster embryo shown in Fig. 5. Slices from a) a view showing the upper left portion of the embryo clearly; b) a view taken at 180° with respect to the orientation in a); c) a view taken at 45° with respect to a). d) Overlay of the slices in a-c). The views in a) and b) contain very little overlapping information, which would make them difficult to register in isolation. Scale bar = 100 μm. See also Movie 4, an animation of progressive construction of a running-mean registration target. Individual cross-sections are shown in the red channel, and the green channel shows the running mean registration target as each view is successively added. [Media 4]
After the initial round of registration, an initial fusion of the images is generated by taking the weighted average of the registered views in Fourier space (see Fig. 7)
Σjw(i͂j)=Σj(wj∙i͂j),
where i͂j are the data sets to be averaged and Wj the corresponding weights. We assume that the image noise is governed by Poisson statistics, so that the weighting of the average is by the expected signal-to-noise ratio (SNR):
wj=∣ij͂∣Σj(∣i͂j∣).
This fusion is then used for a second registration stage, in which the target is the fusion from the first stage (rather than a running sum of the individual views). The process is repeated until the registration corrections become small (typically less than 1 pixel), which generally occurs within at most three iterations. We have found that this iterative procedure, although computationally time-consuming, generally provides a small but significant improvement in the quality of the final reconstruction (data not shown).
3.3.3 Fusion
At this point it is possible to halt the processing; the resulting output is the registered image stacks and the initial, non-iterative fusion. However, as is shown in Fig. 3, a significant further improvement to the fusion can often be gained by fusing the registered images using a multi-view deconvolution (MVD). This requires that the PSFs of the views are known, and that the images are of sufficiently high SNR to allow the deconvolution to be effective. The iterative deconvolution steps are outlined on the right in Fig. 7 [33]. First the PSFs are normalized to an integral of unity and shifted so that they are centered at the origin of the coordinate system. This shift is required so that the deconvolution process does not introduce unwanted shifts which would effectively "unregister" the images.
We have investigated two methods for performing the MVD: an iterated Wiener filter approach (MVD-Wiener), and a MAPGG algorithm (MVD-MAPGG). The MVD-MAPGG algorithm is summarized in Fig. 7 and is an adaptation of previously described work [15]. The MVD-Wiener process (also outlined in Fig. 7) involves creating a set of "simulated images", S͂j, by convolving the initial sample estimate, e, with the PSFs. A set of correction terms, c͂j, are then generated from these using a regularized inverse filter. These correction terms represent a measure of how close the simulated images (what we would expect the images to look like, given the estimated sample fluorophore distribution, e) are to the actual, measured images. These correction terms are then combined by the weighted average described above, and added to the estimate from the previous previous iteration to yield an updated estimate. After constraining the sample distribution to be non-negative (for fluorescent imaging) and normalization, the new estimate is compared with the previous one. If the change is sufficiently small the fusion is complete; if not, another iteration of the deconvolution is performed. The regularization parameter μ was optimized manually by pre-deconvolving a small region of interest in a single view of each data set, and then applying this value in the full MVD. This type of multi-view deconvolution has recently been shown to allow SPIM to exceed the resolution of confocal microscopy, even when the confocal uses lenses with NA > 1.2 [22].
The algorithm (except for the MDV-MAPGG routine) was implemented in MATLAB version 7.0.4 (The MathWorks, Inc.). However, to improve processing speed some of the calculation-intensive modules (e.g. Fourier transforms) are implemented using Visual C# .NET (Microsoft Corp.) and call Intel Integrated Performance Primitives version 4.1 (Intel Corp.). Although the algorithm is not currently implemented such that it can process large multi-view data stacks in real time, it is fast enough to be of use for many non-real-time applications. For the data presented in this work, calculation times varied from a few hours (for the pollen sample, Figs. 3–4, where each view was 256×256×256 voxels) to over-night (for the Medaka embryo, Fig. 6 with 609×450×788 voxels) on a desktop PC with a Xeon 2.8GHz CPU. It is thus possible to fuse time-lapse series of multi-view data sets in order to follow, e.g. organogenesis during embryo development, although such calculations will run over several days.
The MAPPG algorithm was adapted from Verveer & Jovin [15], by allowing more than two input images to be used, and by allowing large data sets to be divided into overlapping blocks, which could be processed separately and subsequently stitched together. This portion of the algorithm was implemented using the programming language Python (www.python.org) with the Numarray extension (www.stsci.edu/resources/ software_hardware/numarray) for numerical computing. The algorithm was executed on a cluster of 10 computers with dual-Pentium 4 2.4 GHz processors and 2GB of memory each, running the Linux operating system with the OpenMosix clustering extension. Execution times for the MAPGG routine varied depending on the data sets, but are usually on the order of an hour provided the calculation was properly distributed over the cluster. The source code used to implement the algorithms described in this manuscript is available on request (contact E.H.K. Stelzer, e-mail stelzer@embl.de).
The method described here for processing multi-view data has been shown to be advantageous under a range of conditions. For samples that can be imaged in their entirety in all views (e.g. the pollen grain in Figs. 3 & 4), the fusion provides isotropic resolution that can be higher than that in any of the unprocessed individual views. At the other extreme, when each view shows only a portion of the specimen (e.g. the Drosophila embryo in Fig. 5) the fusion algorithm can combine these results in a "smooth mosaic" covering the entire sample volume. Partially transparent samples such as the Medaka embryo in Fig. 6 can benefit from both of these effects.
We have demonstrated the algorithm on SPIM data, as this system was specifically designed to generate high-quality 3D multi-view fluorescence microscopy images of biological specimens. Since the technique provides optically sectioned fluorescence images with high signal-to-noise ratios, they are ideal for deconvolution-based image fusion. However, the image processing technique described here is not intrinsically limited to SPIM: in principle it could be applied to traditional wide-field, confocal, or two-photon data stacks, or even non-fluorescent or non-optical 3D imaging techniques. The requirements are 1) that the imaging system be able to generate 3D data stacks along multiple viewing directions, 2) that these views are not significantly distorted and have sufficient overlap, so that the preprocessing plus translation allows them to be registered, 3) that the imaging process be spatially invariant and quantitative (the processing would not be expected to work optimally with data obtained by, e.g. differential interference contrast (DIC), in which the response is a non-linear function of the sample properties), and 4) that the PSF can be determined. The last restriction could be relaxed if one were to substitute a "blind multi-view deconvolution" for the MVD-Wiener and/or MVD-MAPGG routines.
The fusion of multi-view SPIM images permits novel experiments in fields such as embryological gene expression. For instance, the Medaka embryo data shown in Fig. 6 was generated in a feasibility test for a large-scale screen to determine gene expression localization. It is evident from the results presented here that SPIM fusions can provide high-resolution information about 3D gene expression patterns throughout entire embryos.
The authors thank Christina Cardoso and Harald Saumweber for kindly donating the nuclei-labeled Drosophila sample, and Mirana Ramialison and Jochen Wittbrodt for the raw Medaka data.
1. S. Hell and E.H.K. Stelzer, "Properties of a 4Pi confocal fluorescence microscope" J. Opt. Soc. Am. A 9, 2159–2166 (1992). [CrossRef]
2. E.H.K. Stelzer and S. Lindek, "Fundamental reduction of the observation volume in far-field light microscopy by detection orthogonal to the illumination axis: confocal theta microscopy" Opt. Commun. 111, 536–547 (1994). [CrossRef]
3. W. Drexler, "Ultrahigh-resolution optical coherence tomography" J. Biomed. Opt. 9, 47–74 (2004). [CrossRef] [PubMed]
4. M.G.L. Gustafsson, D.A. Agard, and J.W. Sedat, "I5M: 3D widefield light microscopy with better than 100 nm axial resolution" J. Microsc. 195, 10–16 (1999). [CrossRef] [PubMed]
5. T.A. Klar, S. Jakobs, M. Dyba, A. Egner, and S.W. Hell, "Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission" PNAS 97, 8206–8210 (2000). [CrossRef] [PubMed]
6. S. Kawata, "The optical computed tomography microscope" in Advances in Optical and Electron Microscopy, Vol. 14, T. Mulvey and C.R.J. Sheppard, eds., (Academic Press Limited, San Diego, 1994).
7. S. Kikuchi, K. Sonobe, L.S. Sidharta, and N. Ohyama, "Three-dimensional computed tomography for optical microscopes" Opt. Commun. 107, 432–444 (1994). [CrossRef]
8. J. Sharpe, et al. "Optical projection tomography as a tool for 3D microscopy and gene expression studies" Science 296, 541–545 (2002). [CrossRef] [PubMed]
9. A.L. Wilke, S.A. Jordan, J.A. Sharpe, D.J. Price, and I.J. Jackson, "Widespread tangential dispersion and extensive cell death during early neurogenesis in the mouse neocortex" Dev. Biol. 267, 109–118 (2004). [CrossRef]
10. P.J. Shaw, D.A. Agard, Y. Hiraoka, and J.W. Sedat, "Tilted view reconstruction in optical microscopy" Biophys. J. 55, 101–110 (1989). [CrossRef] [PubMed]
11. J. Bradl, M. Hausmann, V. Ehemann, D. Komitowski, and C. Cremer, "A tilting device for three-dimensional microscopy: application to in situ imaging of interphase cell nuclei" J. Microsc. 168, 47–57 (1992). [CrossRef] [PubMed]
12. C.J. Cogswell, K.G. Larkin, and H.U. Klemm, "Fluorescence microtomography: multi-angle image acquisition and 3D digital reconstruction" SPIE Proc. 2655, 109–115 (1996). [CrossRef]
13. M. Kozubek, et al. "Automated microaxial tomography of cell nuclei after specific labeling by fluorescence in situ hybridisation" Micron 33, 655–665 (2002). [CrossRef] [PubMed]
14. J. Bradl, M. Hausmann, B. Schneider, B. Rinke, and C. Cremer, "A versatile 27t-tilting device for fluorescence microscopes" J. Microsc. 176, 211–221 (1994). [CrossRef]
15. P.J. Verveer and T.M. Jovin, "Improved resolution from multiple images of a single object: application to fluorescence microscopy" Appl. Opt. 37, 6240–6246 (1998). [CrossRef]
16. J. Huisken, J. Swoger, F. Del Bene, J. Wittbrodt, and E.H.K. Stelzer, "Optical sectioning deep inside live embryos by selective plane illumination microscopy" Science 305, 1007–1009 (2004). [CrossRef] [PubMed]
17. A.H. Voie, "Imaging the intact guinea pig tympanic bulla by orthogonal-plane fluorescence optical sectioning microscopy", Hearing Research 171, 119–128 (2002). [CrossRef] [PubMed]
18. H.-U. Dodt, U. Leischner, A. Schierloh, N. Jährling, C.P. Mauch, K. Deininger, J.M. Deussing, M. Eder, W Zieglgänsberger, and K. Becker, "Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain" Nat. Methods 4, 331–336 (2007). [CrossRef] [PubMed]
19. P.J. Verveer, J. Swoger, F. Pampaloni, K. Greger, M. Marcello, and E.H.K Stelzer, "'High-resolution three-dimensional imaging of large specimens with light sheet-based microscopy'"Nat. Methods, 4, 311–313 (2007). [PubMed]
20. K. Greger, J. Swoger, and E.H.K. Stelzer, "Basic building units and properties of a fluorescence single plane illumination microscope" Rev. Sci. Instrum. 78, 023705 (2007). [CrossRef] [PubMed]
21. A. Abbott, "Biology's new dimension" Nature 424, 870–872 (2003). [CrossRef] [PubMed]
22. M. Born and E. Wolf, Principles of optics, 7th Ed. Cambridge University Press, Cambridge, CB2 2RU, U.K., 1999.
23. K. Sätzler and R. Eils, "Resolution improvement by 3-D reconstructions from tilted views in axial tomography and confocal theta microscopy" Bioimaging 5, 171–182 (1997). [CrossRef]
24. S. Kikuchi, K. Sonobe, and N. Ohyama, "Three-dimensional microscopic computed tomography based on generalized Radon transform for optical imaging systems" Opt. Commun. 123, 725–733 (1996). [CrossRef]
25. R.A. Brooks and G. Di Chiro, "Theory of image reconstruction in computed tomography" Radiology 117, 561–572 (1975). [PubMed]
26. M. Frasch, "The maternally expressed Drosophila gene encoding the chromatin-binding protein BJ1 is a homolog of the vertebrate gene Regulator of Chromatin Condensation, RCC1" EMBO J. 10, 1225–1236 (1991). [PubMed]
27. A. A. Gortchakov, et al. "Chriz, a chromodomain protein specific for the interbands of Drosophila melanogaster polytene chromosomes" Chromosoma 114, 54–65 (2005). [CrossRef] [PubMed]
28. See the Medaka Expression Pattern Database (MEPD), http://pubservl.embl.de:8280/pubserv/servlet/de.embl.th.mepd.servlets.MdbShowClone01?cloneID=1251.
29. R. Quiring, et al. "Large-scale expression screening by automated whole-mount in situ hybridization" Mech. Dev. 121, 971–976 (2004). [CrossRef] [PubMed]
30. C.J. Engelbrecht and E.H.K. Stelzer, "Resolution enhancement in a light-sheet-based microscope (SPIM)" Opt. Lett. 31, 1477–1479 (2006). [CrossRef] [PubMed]
31. J. Swoger, J. Huisken, and E.H.K. Stelzer, "Multiple imaging axis microscopy improves resolution for thick-sample applications" Opt. Lett. 28, 1654–1656 (2003). [CrossRef] [PubMed]
32. L.G. Brown, "A survey of image registration techniques" ACM Computing Surveys 24, 325–376 (1992). [CrossRef]
33. W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery, Numerical Recipes in C++, 2nd Ed. (Cambridge University Press, Cambridge, U.K., 2002).
S. Hell and E.H.K. Stelzer, "Properties of a 4Pi confocal fluorescence microscope" J. Opt. Soc. Am. A 9, 2159–2166 (1992).
E.H.K. Stelzer and S. Lindek, "Fundamental reduction of the observation volume in far-field light microscopy by detection orthogonal to the illumination axis: confocal theta microscopy" Opt. Commun. 111, 536–547 (1994).
W. Drexler, "Ultrahigh-resolution optical coherence tomography" J. Biomed. Opt. 9, 47–74 (2004).
M.G.L. Gustafsson, D.A. Agard, and J.W. Sedat, "I5M: 3D widefield light microscopy with better than 100 nm axial resolution" J. Microsc. 195, 10–16 (1999).
T.A. Klar, S. Jakobs, M. Dyba, A. Egner, and S.W. Hell, "Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission" PNAS 97, 8206–8210 (2000).
S. Kawata, "The optical computed tomography microscope" in Advances in Optical and Electron Microscopy, Vol. 14, T. Mulvey and C.R.J. Sheppard, eds., (Academic Press Limited, San Diego, 1994).
S. Kikuchi, K. Sonobe, L.S. Sidharta, and N. Ohyama, "Three-dimensional computed tomography for optical microscopes" Opt. Commun. 107, 432–444 (1994).
J. Sharpe, et al. "Optical projection tomography as a tool for 3D microscopy and gene expression studies" Science 296, 541–545 (2002).
A.L. Wilke, S.A. Jordan, J.A. Sharpe, D.J. Price, and I.J. Jackson, "Widespread tangential dispersion and extensive cell death during early neurogenesis in the mouse neocortex" Dev. Biol. 267, 109–118 (2004).
P.J. Shaw, D.A. Agard, Y. Hiraoka, and J.W. Sedat, "Tilted view reconstruction in optical microscopy" Biophys. J. 55, 101–110 (1989).
J. Bradl, M. Hausmann, V. Ehemann, D. Komitowski, and C. Cremer, "A tilting device for three-dimensional microscopy: application to in situ imaging of interphase cell nuclei" J. Microsc. 168, 47–57 (1992).
C.J. Cogswell, K.G. Larkin, and H.U. Klemm, "Fluorescence microtomography: multi-angle image acquisition and 3D digital reconstruction" SPIE Proc. 2655, 109–115 (1996).
M. Kozubek, et al. "Automated microaxial tomography of cell nuclei after specific labeling by fluorescence in situ hybridisation" Micron 33, 655–665 (2002).
J. Bradl, M. Hausmann, B. Schneider, B. Rinke, and C. Cremer, "A versatile 27t-tilting device for fluorescence microscopes" J. Microsc. 176, 211–221 (1994).
P.J. Verveer and T.M. Jovin, "Improved resolution from multiple images of a single object: application to fluorescence microscopy" Appl. Opt. 37, 6240–6246 (1998).
J. Huisken, J. Swoger, F. Del Bene, J. Wittbrodt, and E.H.K. Stelzer, "Optical sectioning deep inside live embryos by selective plane illumination microscopy" Science 305, 1007–1009 (2004).
A.H. Voie, "Imaging the intact guinea pig tympanic bulla by orthogonal-plane fluorescence optical sectioning microscopy", Hearing Research 171, 119–128 (2002).
H.-U. Dodt, U. Leischner, A. Schierloh, N. Jährling, C.P. Mauch, K. Deininger, J.M. Deussing, M. Eder, W Zieglgänsberger, and K. Becker, "Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain" Nat. Methods 4, 331–336 (2007).
P.J. Verveer, J. Swoger, F. Pampaloni, K. Greger, M. Marcello, and E.H.K Stelzer, "'High-resolution three-dimensional imaging of large specimens with light sheet-based microscopy'"Nat. Methods, 4, 311–313 (2007).
K. Greger, J. Swoger, and E.H.K. Stelzer, "Basic building units and properties of a fluorescence single plane illumination microscope" Rev. Sci. Instrum. 78, 023705 (2007).
A. Abbott, "Biology's new dimension" Nature 424, 870–872 (2003).
M. Born and E. Wolf, Principles of optics, 7th Ed. Cambridge University Press, Cambridge, CB2 2RU, U.K., 1999.
K. Sätzler and R. Eils, "Resolution improvement by 3-D reconstructions from tilted views in axial tomography and confocal theta microscopy" Bioimaging 5, 171–182 (1997).
S. Kikuchi, K. Sonobe, and N. Ohyama, "Three-dimensional microscopic computed tomography based on generalized Radon transform for optical imaging systems" Opt. Commun. 123, 725–733 (1996).
R.A. Brooks and G. Di Chiro, "Theory of image reconstruction in computed tomography" Radiology 117, 561–572 (1975).
M. Frasch, "The maternally expressed Drosophila gene encoding the chromatin-binding protein BJ1 is a homolog of the vertebrate gene Regulator of Chromatin Condensation, RCC1" EMBO J. 10, 1225–1236 (1991).
A. A. Gortchakov, et al. "Chriz, a chromodomain protein specific for the interbands of Drosophila melanogaster polytene chromosomes" Chromosoma 114, 54–65 (2005).
See the Medaka Expression Pattern Database (MEPD), http://pubservl.embl.de:8280/pubserv/servlet/de.embl.th.mepd.servlets.MdbShowClone01?cloneID=1251.
R. Quiring, et al. "Large-scale expression screening by automated whole-mount in situ hybridization" Mech. Dev. 121, 971–976 (2004).
C.J. Engelbrecht and E.H.K. Stelzer, "Resolution enhancement in a light-sheet-based microscope (SPIM)" Opt. Lett. 31, 1477–1479 (2006).
J. Swoger, J. Huisken, and E.H.K. Stelzer, "Multiple imaging axis microscopy improves resolution for thick-sample applications" Opt. Lett. 28, 1654–1656 (2003).
L.G. Brown, "A survey of image registration techniques" ACM Computing Surveys 24, 325–376 (1992).
W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery, Numerical Recipes in C++, 2nd Ed. (Cambridge University Press, Cambridge, U.K., 2002).
Abbott, A.
Agard, D.A.
Becker, K.
Bene, F. Del
Born, M.
Bradl, J.
Brooks, R.A.
Brown, L.G.
Chiro, G. Di
Cogswell, C.J.
Cremer, C.
Deininger, K.
Deussing, J.M.
Dodt, H.-U.
Drexler, W.
Dyba, M.
Eder, M.
Egner, A.
Ehemann, V.
Eils, R.
Engelbrecht, C.J.
Flannery, B.P.
Frasch, M.
Gortchakov, A. A.
Greger, K.
Gustafsson, M.G.L.
Hausmann, M.
Hell, S.
Hell, S.W.
Hiraoka, Y.
Huisken, J.
Jackson, I.J.
Jährling, N.
Jakobs, S.
Jordan, S.A.
Jovin, T.M.
Kawata, S.
Kikuchi, S.
Klar, T.A.
Klemm, H.U.
Komitowski, D.
Kozubek, M.
Larkin, K.G.
Leischner, U.
Lindek, S.
Marcello, M.
Mauch, C.P.
Ohyama, N.
Pampaloni, F.
Press, W.H.
Price, D.J.
Quiring, R.
Rinke, B.
Sätzler, K.
Schierloh, A.
Schneider, B.
Sedat, J.W.
Sharpe, J.
Sharpe, J.A.
Shaw, P.J.
Sidharta, L.S.
Sonobe, K.
Stelzer, E.H.K
Stelzer, E.H.K.
Swoger, J.
Teukolsky, S.A.
Verveer, P.J.
Vetterling, W.T.
Voie, A.H.
Wilke, A.L.
Wittbrodt, J.
Wolf, E.
Zieglgänsberger, W
ACM Computing Surveys (1)
Bioimaging (1)
Biophys. J. (1)
Chromosoma (1)
Dev. Biol. (1)
EMBO J. (1)
Hearing Research (1)
J. Biomed. Opt. (1)
J. Microsc. (3)
Mech. Dev. (1)
Nat. Methods (1)
PNAS (1)
SPIE Proc. (1)
Supplementary Material (4)
Media 1: MOV (1550 KB)
Media 4: MOV (99 KB)
Fig. 7. Image processing algorithm. Inputs: N, number of views; ij , j th image; ϕj , j th viewing angle; M, optical magnification; ∆x, pixel pitch (lateral); ∆z, slice spacing (axial); f HP, high-pass spatial frequency filter; r max, maximum registration error; pj , j th PSF; μ, MVD-Wiener regularization parameter; k max, MVD-Wiener termination parameter; g σ, MVD-MAPGG Gaussian filter; k max, MVD-MAPGG maximum number of iterations. Output: e, sample distribution estimate. Internal: r⃑ = (x,y,z), position; s⃑, spatial frequency; I⃑ , mean image integral; e 0, registration target; xj , j th cross-correlation; r⃑, position of peak of xj ; r, gradient vector; d, search direction; α, step size along d. Functional: Σ w j , weighted average (see text); CGA, conjugate gradient algorithm [3]; MAPGG, maximum a posteriori with Gaussian noise and prior algorithm [15]; *, convolution operator; ⊗, correlation operator; ã , Fourier transform of a.
(1) Σ j w ( i ͂ j ) = Σ j ( w j ∙ i ͂ j ) ,
(2) w j = ∣ i j ͂ ∣ Σ j ( ∣ i ͂ j ∣ ) .


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text:
Q: C# Func and Conditional Operator Duplicate
I can do this:
Func<CategorySummary, decimal> orderByFunc;
if (orderBy == OrderProductsByProperty.Speed)
     orderByFunc = x => x.Speed;
else
    orderByFunc = x => x.Price;

Why can't I do this:
Func<CategorySummary, decimal> orderByFunc = (orderBy == OrderProductsByProperty.Speed) ? x => x.Speed : x => x.Price;

A: The 'type inference' on the conditional operator is not quite good enough, I get a message like

Type of conditional expression cannot
  be determined because there is no
  implicit conversion between 'lambda
  expression' and 'lambda expression'

you can always just be explicit on the right-hand-side, a la
var o = true ? new Func<int,int>(x => 0) : new Func<int,int>(x => 1);

In any case it's just a minor annoyance regarding how the types of lambdas, type inference, and the conditional operator interact.

A: Just cast the lambda's to Func<CategorySummary, decimal> and it will work

A: An alternative to the suggestions so far - move the conditional inside the lambda expression:
Func<CategorySummary, decimal> orderByFunc =
    x => (orderBy == OrderProductsByProperty.Speed) ? x.Speed : x.Price;

It may not be suitable in all situations (and it does mean that the check is performed on every invocation) but sometimes it could be useful.
EDIT: As Eric points out, the two are not equivalent. Here's a quick example of how they differ (using explicit casting to get the conditional to work where the operands are lambdas):
using System;

class Test
{
    static void Main()
    {
        bool likesCheese = false;

        Action outerConditional = likesCheese
            ? (Action) (() => Console.WriteLine("Outer: I like cheese"))
            : (Action) (() => Console.WriteLine("Outer: I hate cheese"));

        Action innerConditional = () =>
            Console.WriteLine (likesCheese ? "Inner: I like cheese"
                                           : "Inner: I hate cheese");

        Console.WriteLine("Before change...");
        outerConditional();
        innerConditional();

        likesCheese = true;
        Console.WriteLine("After change...");
        outerConditional();
        innerConditional();
    }
}

Results:
Before change...
Outer: I hate cheese
Inner: I hate cheese
After change...
Outer: I hate cheese
Inner: I like cheese

As you can see, the change to the value of likesCheese only affects the version which has the conditional operator inside the lambda expression. Sometimes this is desirable, sometimes not... but you definitely need to be aware of it.

A: It's enough to cast only one resulting operand to a target type:
Action showResult = true
    ? (Action)(() => Console.Write("Hello!"))
    : () => Console.Write("");

It was a little unexpected, but this is how compiler works. And when I think about it, it makes sense now.

redpajama_set_name: RedPajamaStackExchange


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text:
#ifndef STRUCTS_H
#define STRUCTS_H

#include <sys/time.h>

struct myproc
{
	pid_t pid, ppid;
  int jid;

	uid_t uid;
  gid_t gid;
  gid_t pgrp;
	char *unam, *gnam;

	char *shell_cmd;      /* allocated, from argv */
	char **argv;          /* allocated */
  size_t argc;
	char *argv0_basename; /* pointer to somewhere in argv[0] */

  enum proc_state
	{
		PROC_STATE_RUN,
		PROC_STATE_SLEEP,
		PROC_STATE_DISK,
		PROC_STATE_STOPPED,
		PROC_STATE_ZOMBIE,
		PROC_STATE_DEAD,
		PROC_STATE_TRACE,
		PROC_STATE_OTHER,
#define PROC_N_STATES (PROC_STATE_OTHER + 1)
	} state;

  char *tty;
  signed char nice;

	double pc_cpu;
	unsigned long utime, stime, cutime, cstime;
	unsigned long cputime;
	unsigned long memsize;

	/* important */
	struct myproc *hash_next;
	struct myproc **children;
	int mark;

	union
	{
		struct
		{
			int flag;
		} freebsd;
	} machine;
};

struct sysinfo
{
	// process info
	int count, count_kernel, owned;
	int procs_in_state[PROC_N_STATES]; /* number of PROC_STATE */

	// machine info
	float loadavg[3];

#ifndef __LP64__
	long
#endif
	unsigned memory[6];

	float cpu_pct;

	unsigned long cpu_cycles;
	int ncpus;

	struct timeval boottime;
};

#endif

redpajama_set_name: RedPajamaGithub


text:
Tag: Congress Party
Politics	 28 Jun 2019 28 Jun 2019
A file photo of Rahul Gandhi
Congress president Rahul Gandhi on Thursday met with Lok Sabha MPs from his party in New Delhi. While rejecting the request of his colleagues to withdraw his resignation as party president, Mr Gandhi said he is resigning to take responsibility for the poll debacle of his party in the recent Lok Sabha elections. He also asserted that he is committed to fighting the RSS and the BJP and has become a fanatic towards this cause.
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Filed under: #Rahul Gandhi, BJP, Congress Party, Congress president, Congress Vs BJP, Lok Sabha 2019 results, Rahul Gandhi's resignation, RSS ideology
Election, Politics	 7 Jun 2019 7 Jun 2019
Aslam Sher Khan offers to lead Congress for two years
Aslam Sher Khan and Rahul Gandhi (Image credit: Twitter and Wikimedia Commons)
Hockey Olympian and former Union minister Aslam Sher Khan has reportedly staked a claim to the Congress president's post, in place of Rahul Gandhi. The news comes even as suspense continues over Rahul's latest stance on quitting the position.
According to Hindustan Times, Khan wrote a letter to the Congress president, offering his "services to the party", taking responsibility "as a provisional Congress president for a time-bound period of two years only."
Khan was part of the Indian hockey team that won the 1975 World Cup at Kuala Lumpur in Malaysia. He joined the Congress and got elected as an MP from Madhya Pradesh in 1984 and 1991. He lost the 2004 Lok Sabha election from Bhopal constituency and 2009 election from Sagar seat, both in Madhya Pradesh. For a brief period, he was also in the BJP (between December 1997 and January 1999).
Khan claimed to have made the offer based on his experience in international sports and politics. Incidentally, he seems to be the only one to have offered to lead the Congress after Rahu Gandhi expressed his desire to relinquish the responsibility of heading the party. .
Gandhi had announced his decision to step down on May 24, following the crushing defeat of his party in the Lok Sabha elections, winning just 52 seats against BJP's 303. However, the party leaders pleaded with him to continue to lead the party and authorised him to overhaul the organisation.
Filed under: #Rahul Gandhi, Aslam Sher Khan, Congress in crisis, Congress Party, Congress president, Indian National Congress, Lok Sabha 2019 results, Lok Sabha Elections 2019, Rahul Gandhi's resignation

redpajama_set_name: RedPajamaCommonCrawl


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Resident 'absolutely livid' as deer 'killed by motorist' in Bushy Park
The deer had to be put down after being found injured in Richmond
Lucy Skoulding
A deer has sadly had to be put down after being found injured in Bushy Park (Image: Darren Pepe/Surrey Advertiser)
In a heartbreaking turn of events, a deer in Bushy Park had to be put down this week.
A manager at Bushy Park, in Richmond-upon-Thames, confirmed the animal was "found injured" and sadly had to be put down.
A post on a Facebook group claims that the deer was hit by a motorist in the West London park and "the driver of the vehicle did not stop".
According to the Facebook post, the incident happened on Wednesday, November 25.
Phil Edwards, Bushy Park's manager, said: "We can confirm that a deer was found injured in Bushy Park and sadly had to be put down.
Bushy Park officials believe the deer was hit by a vehicle, but no witnesses have come forwards (Image: Unsplash)
"We believe it was hit by a vehicle but there were no witnesses.
"If you see an accident please call the on-call Police for Richmond and Bushy Parks on 07920 586546."
Clearly upset, someone shared a post on Facebook from Nextdoor in which a woman describes what happened.
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"I'm absolutely livid!" she wrote.
"Somebody ignored the barriers along Chestnut Avenue this afternoon and managed to hit a stag, causing massive injury to the animal.
"The poor thing was sadly euthanized just before 4pm.
The driver of the vehicle allegedly didn't even stop after hitting the animal (Image: Amanda Cook)
"The driver of the vehicle did not stop, and simply went about their business!"
The person posting went on to share their feelings about how some people treat their local park.
She added: "As an employee of TRP (The Royal Parks), I just have to say that the way some people treat our park and wildlife is genuinely disgusting - and I won't even mention some of the abuse my colleagues and I have to put up with.
"If anyone witnessed this incident, please contact The Royal Parks and/or the police. To the driver of that vehicle, I sincerely hope you have a good amount of damage to your property."
Find out the latest statistics on the crimes which have been reported near where you live by entering your postcode into our handy crime widget below:
They added: "To be clear, these sentiments are my own, and nothing to do with TRP."
MyLondon contacted the Metropolitan Police for comment, but they have not had any reports of this incident as yet.
Want more news? Head to the MyLondon homepage.


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judoarbucies.com
Who invented the canadian space arm
Who Created the Canadarm and What is Canadarm Used For?
There were, however, a few tense moments still to come. Every part of Canadarm had to be thought through from scratch. This did, in fact, virtually eliminate the problems at launch. It is essentially a robot handyman used for a range of tasks outside the orbiting station, including many of the routine tasks done by astronauts during risky spacewalks.
Article by Karl H.
The Canadarm , which made its debut in 1981 and was retired last year is, without a doubt, one of the most famous robots ever in space. The industrial team, led by Spar Aerospace Ltd. Xs of evil: In extremely cold conditions, thermostatically controlled electric heaters protected critical electronics.
Article published June 23, 2013; last modified November 16, 2018.
In 1962 Canada became only the third nation to operate a man-made satellite, joining the Soviet Union and the United States in what was at the time a very exclusive club. It could place such payloads in any position, within 5 centimetres of a desired target. Thomas Mulcair at the CEP convention. Its excellent performance record has inspired several generations of scientists and engineers as they develop new technologies for industry, medicine, and other applications. A few years ago, the agency received a boost of stimulus funding to fund rovers and robotics projects.
Canadarm2 was launched on STS-100 in April 2001. Filed under: Initial heat treatment of the gear blanks discs from which gear teeth are cut would make machining impossible; the alloy would be too hard.
The Canadarm could be thought of as a 15-metre human arm with a wrist, elbow and shoulder. The arm was also repurposed after the Columbia space shuttle disaster of 2003 , which killed seven astronauts during the shuttle's re-entry.
When both Canadarms were in operation, they were sometimes used in tandem; a process nicknamed the "Canadian handshake" in the media. The end effector uses a unique wire-wrapping mechanism to seize targets NASA.
Canadian Space Agency: Facts & Information
Accessed 26 February 2019. The David Florida Laboratory in Ottawa, Canada, is a test bed for satellites before they reach space.
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The largest alloy steel pipe benefit is its recycling performance, which conforms to the national strategy of environment protection, energy saving as well as resources economizing. And the government encourages enlarging the application range of the high pressure alloy steel pipe. At present, the consumption of the alloy steel pipe is nearly half of that in developed country. And enlarging of the application fields of the alloy steel pipe provides wider space for the development of the alloy steel pipe industry. The alloy steel pipe is a kind of seamless steel pipe, which is different from seam steel pipe like black steel pipe. And the alloy steel pipe is called according to the manufacturing material, which the seamless steel pipe is called according to its manufacturing technique.
Another alloy steel pipe benefit is its higher performance. Due to the existence of Cr, the alloy steel pipe has higher high temperature resistance performance, higher low temperature resistance as well as corrosion resistance. Therefore, the alloy steel pipe has a wide application in oil, aerospace, chemical industry, electric power as well as boiler. And the alloy steel pipe can be classified into functional seamless steel pipe and alloy steel pipe with high pressure and heat resistance performance. And using the annealing tempering of the alloy steel pipe is able to change the chemical performance of the alloy steel pipe. Therefore, the properties of the alloy steel pipe are more changeable and have more value than the ordinary seamless steel pipes.
Another alloy steel pipe benefit is that the alloy steel pipe can be used to manufacture round components with the promotion of material using ratio, simplification of the manufacturing process, reduction of the materials and manufacturing hours such as rolling bearing rings and jack covering. And the alloy steel pipe also is possible to be the manufacturing material of the weapons like barrel and gun barrel. According to different shape of sectional area, the alloy steel pipe can be classified into round pipes and special pipes. And under the condition of equal perimeter, the round of the alloy steel pipe is able to reach its maximum area. And using the round pipes is able to transport more liquid. In addition, the round section has uniform press when bearing inner and outer pressure. Therefore, most alloy steel pipes are round pipes.
Another alloy steel pipe benefit is its structure. The alloy steel pipe has hollow section to be used as the pipes to deliver liquid like oil, natural gas, water and solid materials. And compared to solid pipes, the alloy steel pipe has lighter weight under the same bending resistance and twisting resistance performance. And the alloy steel pipe has a kind of economized section which makes it able to be used in manufacturing components and mechanical parts.


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"There's no talent here, this is hard work. This is an obsession. Talent does not exist, we are all equal as human beings. You could be anyone if you put in the time. You will reach the top, and that is that. I am not talented, I am obsessed.


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Hawaii Tribune-Herald on the recent Mauna Kea lawsuit decision
I had posted last week about the dismissal of a lawsuit by the opponents of telescope construction on Mauna Kea (for a background of the relevant issues, see this post). Now, Hawaii Tribune-Herald points out that the 3rd Circuit ruling may not have ended further lawsuits. But first, here is a nice summary of what prompted the current lawsuit about the Comprehensive Management Plan (CMP) approved by the Board of Land and Natural Resources (BLNR):
The Mauna Kea CMP is a wide-ranging plan to manage the vast cultural and natural resources of Mauna Kea. It was developed in response to a 2007 ruling in Hara's court that the mountain lacked a comprehensive management plan, delivering a victory to Mauna Kea Anaina Hou, the Sierra Club, the Royal Order of Kamehameha I and Clarence Ching.
The UH Office of Mauna Kea Management then developed its plan and presented it to the Board of Land and Natural Resources, which passed it at the end of a two-day hearing on April 9. The BLNR denied the request by those same groups, plus KAHEA, for a contested case hearing on Aug. 28, and the decision was appealed to the Circuit Court on Oct. 1. UH attorneys countered with a motion for dismissal.
Hara heard arguments from both sides on Dec. 9 and ruled for the university on Dec. 29. He decided that the April 8-9 public hearing on the CMP did not determine the rights, duties or privileges of the plan's opponents, which would have triggered a requirement for a contested case hearing.
The judge found that the groups failed to prove the land board's passage of the plan adversely affected them, and that the 2007 ruling did not give them any added interest that would have entitled them to a contested case hearing.
But it seems that the actual implementation of the CMP (rather than just the approval) may still prompt more lawsuits. However, I doubt any further lawsuits will stick. The University of Hawaii this time organized much better, took the criticism of Native Hawaiians seriously (there may still be differences in opinion, but at least they did not try to roll over them), and, as far as I can tell, tried to follow the rules with future lawsuits in mind. But lawsuits can still delay the start of construction of the Thirty Meter Telescope (TMT) and that can be quite costly for the Consortium:
Hara's ruling has a potential silver lining for those who oppose the plan on the grounds that it is poorly written and should not have been prepared by the same entity that seeks to develop astronomy facilities there.
"It may be," Hara wrote, "that a future implementation of the CMP might trigger a requirement for a contested case, but the action of the BLNR in accepting and approving the CMP in and of itself does not do so."
"We're disappointed," said the Sierra Club's Nelson Ho, but he added that "it may become justice delayed rather than justice denied."
The CMP "remains an unimplemented plan," Hara wrote. If the university never implements its recommendations, then the rights of the plan's opponents would not be affected, a contested case hearing is not required and the judge cannot consider an appeal.
Ho said the coalition of Hawaiian and environmental groups -- all opposed to the university's management of the mountain -- could appeal Hara's decision to a higher court.
"Or we could wait for the next opportunity," Ho said. "What is that and when is that, and how do we raise our points that this is not a good comprehensive management plan?"
Kealoha Pisciotta, an ally of Ho and president of Mauna Kea Anaina Hou, could not say what advice attorney Colin Yost would give, but she pledged to continue fighting in some way.
"Mauna Kea belongs to the people, and for the Hawaiian people it's a temple and a burial ground. And we're not giving up in any way."
Posted by Salman Hameed at 6:08 PM
Labels: Astronomy, politics of science and religion, science and Native religions
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Life & Work of Ralph DiGia
My Resistance to World War II
Keeping Alive the Flame of Resistance
With the Peace Walkers in Albany, Georgia
Timeline of a Life of Activism
And Death No Dominion
Death and Taxes…and a whole lot of fun
A Man of Peace
Support the Fund
Guestbook Added
A guestbook has been added to the site! It can be found under the 'Remembrances' heading or you can click here. Please take a moment to sign and share a memory or story about Ralph.
Written by Daniel, updated 5/5/08
80 Years of Activism Ends
December 13, 1914 – February 1, 2008
Ralph DiGia, World War II conscientious objector, lifelong pacifist and social justice activist, and staffer for 52 years at the War Resisters League (WRL), died February 1 in New York City. He was 93. (more…)
Written by Daniel, updated 12/4/08
Copyright © 2008 Ralph DiGia Fund for Peace & Justice
The Ralph DiGia Fund for Peace & Justice is a not-for-profit section 501 (c)(3) exempt private foundation.


Man Utd news: Nemanja Vidic and Patrice Evra DESTROYED by under-21s in first game - Daily Star Daily StarMANCHESTER UNITED legend Nemanja Vidic revealed how his first game with Patrice Evra was against the under-21s – and did not end well.
13 signings Manchester United missed out on - and who they signed instead PlanetFootballManchester United have made some notable signings en route to a record number of Premier League titles, but there were still a few who got away.
Ten classic Champions League matches involving English clubs including Liverpool 3-3 AC Milan Sportinglife.comRecovered from watching Wednesday night's unbelievable Champions League action? We've got 10 classic encounters involving English clubs to look back on.
Michael Clegg on Man Utd, depression, early retirement and Roy Keane's help PlanetFootballA sliding doors moment may have opened just enough to give Michael Clegg a long and successful career at Manchester United – but it wasn't meant to be.
Manchester United news and transfers LIVE remaining fixtures and Koulibaly latest Manchester Evening NewsWe'll bring you all the latest Man Utd news, views and transfer rumours throughout the day as preparations begin for Everton on Sunday.
BBC Sport pundit: Sir Alex Ferguson tipping Liverpool FC to win the title The Sport ReviewSir Alex Ferguson has backed Liverpool FC to beat Manchester City to the Premier League title this season, according to former Stoke striker Jonathan.
Manchester United might have found their perfect transfer target Manchester Evening NewsMan Utd are set for a summer rebuild after crashing out of the Champions League to Barcelona and need a number of new signings.
Solskjaer facing early crossroads as United's limitations laid bare The Irish TimesFirst priority will be ensuring side qualify for Champions League next season.
Sir Alex Ferguson 'has predicted the title race between Liverpool and Man City' SquawkaWith Liverpool and Man City neck and neck in the Premier League title race, Sir Alex Ferguson predicts the winner.
Lansing area high school boys golf overview Lansing State JournalMason's Alex Jordan and DeWitt's Charlie DeLong highlight the returners in the area.
Talking Point: What is behind Manchester United's recent slump? The World GameAfter a sensational Premier League start under Ole Gunnar Solskjaer, why have Manchester United tailed off in recent weeks?
Arsenal: What to do with Alex Iwobi FanSidedWhat does the future hold for Alex Iwobi? The midfielder is extremely talented and yet unproductive. How should Arsenal handle his position and potential?
Henderson proves Sir Alex wrong by becoming Klopp's trusted man Khaleej TimesThe 28-year-old has won Klopp's trust so completely that he goes into Wednesday's Champions League quarterfinal second leg at Porto.
Who is the Best manager? Reasons why aswell please!
Please can somebody tell me or provide me with a link where I can obtain Sir Alex Ferguson's Best XI? and I mean the exact list of players he said not what other journalists think or give opinions on.
What would be Alex Ferguson's excuse if he loses this time?????????? or Pep?


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Brown Pelican Facts | Anatomy, Diet, Habitat, Behavior
The brown pelican (Pelecanus occidentalis) is a heavyweight hunter that flies American coastlines in large aerial formations. It is the only pelican species that dives into the sea to catch fish. The bird is a relatively common species as it often visits tourist beaches. The pelican patrols west coast of America from California to as far as northeastern Brazil.
Brown Pelican Facts
The brown pelican is smallest of the pelican species but it is greater than most other birds. The bird reaches a length of 106–137 cm (42–54 in) and weighs up to 2.75 to 5.5 kg (6.1 to 12.1 lb).
The wingspan measures around 1.83 to 2.5 m (6.0 to 8.2 ft). The brown pelican is quite unique in its range.
These pelicans are recognized by their extraordinary large greyish bill that averages 28 to 34.8 cm (11.0 to 13.7 in) in length. There is a gular at the bottom of the pelican's bill which drains out water when the bird scoops out the fish.
The brown pelican breeds in the east and west coast of America. The bird's range goes from California, northern Chile, northeastern Brazil, to the Caribbean and Galapagos Islands.
Brown pelicans are widely distributed across Nova Scotia, Venezuela, and Gulf of Mexico. It is less likely to breed in the north of the Carolinas.
Courtesy www.twoshutterbirds.com
Brown pelicans make homes in habitats such as mangrove swamps but they also prefer freshwater lakes. Before the arrival of winter, the bird will fly towards warmer water.
They rarely fly away to inland lagoons and wander out to sea. Brown pelicans purely breed in coastlines. The habitat includes rocky shores in Baja California and beach resorts off western South America.
Feeding Ecology and Diet
Fish makes much of the brown pelican's diet but they also prey on amphibians and rarely on crustaceans. Prominent among fish is menhaden which accounts for 90 – 95% of brown pelican's diet.
The brown pelican supplements its diet with anchovy—a food that plays a major role in the nesting success of the bird.
The diet also includes pigfish, pinfish, herring, sheepshead, silversides, mullet, minnows, and prawns.
The adult brown pelican consumes as much as 1.8 kg (4.0 lb) a day.
Brown pelicans are likely to feed and roost together. They are known to gather around in flocks up to 70 birds.
The bird spends most of its time either resting on the water surface, perching on rocks, or simply standing on the shoreline.
They do not beat their wings too often; pelicans take frequent glides.
Pelicans are primarily known for diving and holding prey while flying 10 – 30 inches above the sea.
Although they are sociable birds but they do not make contact calls as many other birds do.
Pelicans fly in a V-shape formation.
LONG BEACH, CA The brown pelican is swooping a fish
Brown pelicans build nests in March and April. These nests are built in bushes, trees, on the ground, or on a series of rock ledges. The bird lines its nest with grasses, sticks, reeds, and straw.
The pelican's nest is 10–26 cm high above the ground.
There are 2 – 3 eggs in a single clutch. The incubation period lasts about 28 – 30 days. After 35 days the young walk independently but they remain in the nest for 68 – 88 days.
Both parents contribute toward rearing the young. They will feed nestlings with 70 kg (150 lb) of fish and the feeding continues up to 8 – 10 months.
Brown pelicans attain the maturity at 3 – 5 years of age.
Predators include foxes, raptors, feral cats, gulls, and skunks.
The average lifespan of brown pelicans is 15 – 25 years.
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Now that flagships from Samsung, HTC, and LG are available or purchase, it's time we look at what's coming from Motorola, since they tend to introduce new Moto X phones around the end of August. With the purchase of Motorola by Lenovo now complete, this year should be especially interesting for the Chicago-based smartphone maker, if anything, to see how Lenovo treats one of its new outlet's product launches.
Rumors have begun to emerge within recent weeks, though you should take all of it in stride, as Motorola leaks in recent years have rarely been accurate. With that said, it's time to start acknowledging some of this stuff, so that we can hopefully start to piece it all together over the next few months as we close in on the release of the Moto X (3rd Gen).
First, model number XT1542 is one to keep an eye on. The number has popped in supposed benchmark screenshots, along with references to a 5.2-inch QHD display (2560×1440), 16MP rear camera, 5MP front camera, 4GB RAM, and a Snapdragon 810 processor (with Adreno 430 GPU).
Those numbers, of course, differ from the set of numbers that popped up over on reddit a month or so ago. Then, we saw numbers of XT1578, XT1585, and XT192A.
Additional rumors suggest that there may actually be two Motorola devices, one with a 5.2-inch display and another with a 5.7-inch QHD display, though that information is completely unconfirmed.
Finally, a rumor floating about today is attempting to suggest that the images below represent a phone that will be outfitted with a fingerprint sensor. See that missing square space in the middle of the phone? I guess that could be for a fingerprint sensor. Then again, Motorola COO Rick Osterloh told CNET in March that he wasn't a fan of fingerprint sensors. If the guy in charge of making Motorola's next phone isn't a fan of fingerprint sensor, then I kind of doubt that this phone's empty square box is for a fingerprint sensor. Just saying.
And that's it for now. We know that Motorola will have a phone for us towards the end of this summer, in order to keep with their yearly Moto X schedule. Other than that, again, take all of this as rumor and speculation, not fact.
If you know anything, be sure to drop us a line.


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Me and Mini-Me – a small adventure in L.A.
Uncategorized | By Tom Henry | No comments
In the early part of 2012 I stayed with the late Verne Troyer for a week, putting together a long pitch for his autobiography, which we all hoped would lift the lid on an extraordinary, inspirational and often chequered life and career in the movie business.
What a week that was…..
At the time of writing, the circumstances around Verne's death hint at tragedy, in which drink and depression appear to play a major role. Yet let no-one say that Verne didn't live a big life, despite his size. A week isn't long enough to get to know anyone, but at least you can take away a snapshot that isn't too blurred around the edges, and by the time I arrived home I'd certainly had a taste of life at the outer limits of Hollywood.
I arrived in L.A., jet-lagged and spooked by the mandatory grilling from US Immigration, to Verne's opening question: "Do you fancy visiting the Playboy Mansion tonight?"
Well, that's some offer….. I wondered if the beds at the Mansion might be comfortable, and whether there was much chance of being able to find a nice, quiet single Bunnyless one where I could nap off my previous 11 hours fastened into Economy. But before I could reply, his very nice manager took me aside. "Verne's not allowed to go to the Mansion," he said, "cos every time he does, he comes back with a gold-digger."! Point taken.
Verne and I spent hours every day, talking over his life-story while sitting in his room, which was furnished by the tiny chairs and sofas made for him on the set of the Austin Powers movie. He had a terrific story to tell, one of absolute determination not to be different from anyone else, despite being just 2ft 8. He was a wild one, alright. Nothing and no-one stopped him from having as much fun as possible. And yet, despite being one of the world's most recognised people (going around LA with him attracted unbelievable amounts of attention everywhere we went) he lived very modestly. He was a manic driver, too. He would tear across the freeways like he was Ayrton Senna. I imagined the headlines… 'Brit writer in superstar dwarf death-smash.' It would've been some way to go.
He also had a very large streak of kindness and thoughtfulness that I think stemmed from his upbringing in the Amish community around Michigan. He knew he would always be stereotyped, yet his ambition was to play the lead in a rom-com that didn't centre around his size. Wishful thinking, I guess, but a noble ambition all the same. He showed me an independent film he'd starred in, called Bit Players. It (and he) was brilliant and if he'd played his cards right he might have been considered for more parts like this. Sadly, I think he was perhaps just a bit too much of a handful for the major studios.
I came back from L.A. looking forward to working with him again later in the year, once the project had sold. Unfortunately, it didn't. This was due to a combination of various elements – not a few publishers, I think, felt repelled by what they perceived as his 'freakishness'. He was upset and frustrated that the project didn't take off, feeling no-one was interested in him. In turn, I felt for him because I knew his was an amazing and inspirational story, but I also thought that one day things would turn around for him and there would be a happy ending. That would then be the time to re-pitch the book.
Obviously, there will be no happy ending now. I just hope that Verne Troyer, an unusual and interesting man in far more ways than the obvious one, is at peace now. RIP, mate.
PS…Verne and I did eventually go to the Playboy Mansion….well, up to the main gate, at least. "Shall I press the buzzer?" he asked me. "We'll have a great time, you know….."
I considered my options, and realised that if things became really heated I could always make my excuses and leave. Bringing a gold-digger back to Somerset would've taken some explaining, I felt. Particularly when she realised there was very little gold to be had. And in life, I've always been more observer than participant, shall we say, and in any case, a few hours of Verne's company in the Mansion would be the stuff of pub conversation for the rest of my days.
"Your call, Verne," I said.
He wound down the window and his finger hovered over the button. Then he withdrew his arm. "Nah," he said. "It'll only get us both into big trouble…."
Testimonial from snooker star Stephen Hendry
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(c) Tom Henry 2014. All rights reserved. Site by e4uwebdesign.com


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Art Fisher (1934–1984)
He is known as the first director to apply chroma key (aka green screen) technology in his Cher Specials. His prodigy David Grossman went on to become a leading TV director, as he is co-head director... read full biography
Known for The Sonny and Cher Comedy Hour (1971-1974), Neil Diamond: I'm Glad You're Here with Me Tonight (1977), The Andy Williams Show (1970), Cher (1975)
Feb 6, 1934 (died Feb 22, 1984)
Become a member to see Art Fisher's STARmeter.
Won 1 Primetime Emmy Award. Another 1 win & 10 nominations
Become a member to see Art Fisher's contact information.
Director (51)
Producer (11)
The Very Best of Cher: The Video Hits Collection (2004)
(Video) - Director (videos: Half-Breed) Director (videos: Half-Breed) See fewer
Cher: Hell on Wheels (1979)
(Video) - Director Director See fewer
Cher: Love & Pain (1979)
Cher: Take Me Home (1979)
Cher: Dark Lady (1974)
Cher: Half-Breed (1973)
Cher: Gypsys, Tramps & Thieves (Version 1) (1971)
Past Television (44 titles)
The Cracker Brothers (1984)
(TV Movie) - Director Director See fewer
Suzanne Somers... And 10,000 G.I.'s (1983)
(TV Special) - Director, Producer Director, Producer See fewer
That's TV (1982)
(TV Movie) - Director, Executive Producer Director, Executive Producer See fewer
The Suzanne Somers Special (1982)
The Tom and Dick Smothers Brothers Special I (1980)
Pink Lady (1980)
(TV Series) - Director (6 episodes, 1980), Production Consultant (6 episodes, 1980) Director (6 episodes, 1980), Production Consultant (6 episodes, 1980) See fewer
Episode #1.6 (Apr 11, 1980) Season 1, Episode 6 - Director, Production Consultant Director, Production Consultant See fewer
Siegfried and Roy (1980)
Cher... and Other Fantasies (1979)
(TV Movie) - Director, Producer Director, Producer See fewer
Donny and Marie (1975–1978)
(TV Series) - Director (26 episodes, 1975), Producer (22 episodes, 1975) Director (26 episodes, 1975), Producer (22 episodes, 1975) See fewer
Episode #4.3 (Oct 13, 1978) Season 4, Episode 3 - Director, Producer Director, Producer See fewer
Disneyland (1978)
(TV Series) - Director (1 episode, 1978) Director (1 episode, 1978) See fewer
NBC Salutes the 25th Anniversary of the Wonderful World of Disney Part 1 (Sep 13, 1978) Season 25, Episode 1 - Director Director See fewer
NBC Salutes the 25th Anniversary of the Wonderful World of Disney (1978)
Cher... Special (1978)
Neil Diamond: I'm Glad You're Here with Me Tonight (1977)
Lindsay Wagner: Another Side of Me (1977)
(TV Special) - Director Director See fewer
The Chevy Chase Show (1977)
The Fantastic Journey (1977)
Funhouse (Mar 31, 1977) Season 1, Episode 7 - Director Director See fewer
Wonderbug (1976)
(TV Series) - Director (5 episodes, 1976) Director (5 episodes, 1976) See fewer
The Not So Great Race (Dec 18, 1976) Season 1, Episode 15 - Director Director See fewer
The Brady Bunch Variety Hour (1976)
Pilot (Nov 28, 1976) Season 1, Episode 0 - Director Director See fewer
The Indian (Oct 8, 1976) Season 1, Episode 3 - Director Director See fewer
Van Dyke and Company (1976)
Episode #1.1 (Sep 20, 1976) Season 1, Episode 1 - Director Director See fewer
Neil Sedaka Steppin' Out (1976)
The Krofft Supershow (1976)
Episode #1.1 (Sep 11, 1976) Season 1, Episode 1 - Director (segments: Kaptain Kool and the Kongs) Director (segments: Kaptain Kool and the Kongs) See fewer
ABC's Saturday Sneak Peek (1976)
Petrocelli (1976)
Survival (Jan 28, 1976) Season 2, Episode 14 - Director Director See fewer
Happy Days (1975)
Jailhouse Rock (Oct 21, 1975) Season 3, Episode 7 - Director Director See fewer
Episode #1.8 (Apr 6, 1975) Season 1, Episode 8 - Director Director See fewer
The Hudson Brothers Razzle Dazzle Show (1974–1975)
(TV Series) - Director Director See fewer
The Sonny Comedy Revue (1974)
The Sonny and Cher Comedy Hour (1971–1974)
(TV Series) - Director (65 episodes, 1971) Director (65 episodes, 1971) See fewer
Episode #4.23 (Mar 6, 1974) Season 4, Episode 23 - Director Director See fewer
Burt and the Girls (1973)
Burt Reynolds' Late Show (1973)
Ann-Margret: When You're Smiling (1973)
A Picture of Us (1973)
Dean Martin Presents: The Bobby Darin Amusement Co. (1972)
Episode #1.4 (Aug 17, 1972) Season 1, Episode 4 - Director Director See fewer
Monsanto Presents Mancini (1971)
(TV Special) - Director, Producer, Writer Director, Producer, Writer See fewer
The Andy Williams Show (1970)
The Ray Stevens Show (1970)
Episode #1.4 (Jul 11, 1970) Season 1, Episode 4 - Director Director See fewer
Episode #1.1 (Jun 20, 1970) Season 1, Episode 1 - Director Director See fewer
Dionne Warwick- Souled Out (1969)
The Andy Williams Magic Lantern Show Company (1969)
33 1/3 Revolutions Per Monkee (1969)
(TV Movie) - Director, Writer Director, Writer See fewer
Andy's Love Concert (1969)
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