You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review on the Extraction of Quantitative Retinal Microvascular Image Feature 
Chapter Title:  Grey-Scale 
1. Abstract of Cited Paper (BIB001):  Conversion of a color image into a grayscale image inclusive of salient features is a complicated process. The converted grayscale image may lose contrasts, sharpness, shadow, and structure of the color image. To preserve contrasts, sharpness, shadow, and structure of the color image a new algorithm has proposed. To convert the color image into grayscale image the new algorithm performs RGB approximation, reduction, and addition of chrominance and luminance. The grayscale images generated using the algorithm in the experiment confirms that the algorithm has preserved the salient features of the color image such as contrasts, sharpness, shadow, and image structure. 
2. Abstract of Cited Paper (BIB002):  This paper presents a framework for the detection and measurement of retinal vessels in fundoscopy images. The proposed method segments retinal vessels, enabling the actual measurement of the vessel diameter. A Graphical User Interface (GUI) further supports a number of methods for the automatic and interactive measurement of vessel diameters at any selected point or region of interest. In addition, it provides methods for editing the vessel representation in order to recover from possible segmentation misclassifications. All the above steps are integrated into a clinical application capable of supporting vascular risk stratification in persons with hypertension. The performance of the segmentation is assessed on two publicly available DRIVE and STARE datasets. The experimental results show that the method can achieve a high accuracy on both datasets with very low computational cost, being comparative or slightly better than widely used supervised and unsupervised methods.

Literature Review:
(1) Here I gray is grey-scale image, I color is color image, = 0.2989, =0.5870, =0.1140, (n,m) is pixel location with grey-scale image, and (n,m,c) is a channel at pixel location (n,m) in the color image for channel c in red r, blue b, and green image channel  . There is the possibility of losing the important features of an image like structure, sharpness, contrast, and shadow during the conversion of the color image into grey-scale image. The study of BIB002 proposed an algorithm for converting the RGB image into the grey-scale image which can do RGB approximation, reduction, and addition of luminance and chrominance and sustain the structure, sharpness, contrast, and shadow of the original RGB color image in the resultant image. The study of BIB001 presented a formula based on the Singular Value Decomposition (SVD) to measure the graphical and scaler values of distortion caused by the noise sources. As a step of fundus retinal image processing, the raw RGB color image is converted into grey-scale using the green channel because the blood vessels of retinal microvasculature appear more contrasted through green channel BIB003 . Figure 9 shows the original image (a) and grey-scale image (b) after conversion.