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:  Hard Exudates Detection. 
1. Abstract of Cited Paper (BIB001):  In the framework of computer assisted diagnosis of diabetic retinopathy, a new algorithm for detection of exudates is presented and discussed. The presence of exudates within the macular region is a main hallmark of diabetic macular edema and allows its detection with a high sensitivity. Hence, detection of exudates is an important diagnostic task, in which computer assistance may play a major role. Exudates are found using their high grey level variation, and their contours are determined by means of morphological reconstruction techniques. The detection of the optic disc is indispensable for this approach. We detect the optic disc by means of morphological filtering techniques and the watershed transformation. The algorithm has been tested on a small image data base and compared with the performance of a human grader. As a result, we obtain a mean sensitivity of 92.8% and a mean predictive value of 92.4%. Robustness with respect to changes of the parameters of the algorithm has been evaluated. 
2. Abstract of Cited Paper (BIB002):  Abstract We present an automatic image processing algorithm to detect hard exudates. Automatic detection of hard exudates from retinal images is an important problem since hard exudates are associated with diabetic retinopathy and have been found to be one of the most prevalent earliest signs of retinopathy. The algorithm is based on Fisher's linear discriminant analysis and makes use of colour information to perform the classification of retinal exudates. We prospectively assessed the algorithm performance using a database containing 58 retinal images with variable colour, brightness, and quality. Our proposed algorithm obtained a sensitivity of 88% with a mean number of 4.83 ± 4.64 false positives per image using the lesion-based performance evaluation criterion, and achieved an image-based classification accuracy of 100% (sensitivity of 100% and specificity of 100%). 
3. Abstract of Cited Paper (BIB003):  The detection of bright objects such as optic disc (OD) and exudates in color fundus images is an important step in the diagnosis of eye diseases such as diabetic retinopathy and glaucoma. In this paper, a novel approach to automatically segment the OD and exudates is proposed. The proposed algorithm makes use of the green component of the image and preprocessing steps such as average filtering, contrast adjustment, and thresholding. The other processing techniques used are morphological opening, extended maxima operator, minima imposition, and watershed transformation. The proposed algorithm is evaluated using the test images of STARE and DRIVE databases with fixed and variable thresholds. The images drawn by human expert are taken as the reference images. The proposed method yields sensitivity values as high as 96.7%, which are better than the results reported in the literature. 
4. Abstract of Cited Paper (BIB004):  Abstract Diabetic Retinopathy is one of the leading causes of blindness in developed countries. Hard exudates have been found to be one of the most prevalent earliest clinical signs of retinopathy. Thus, automatic detection of hard exudates from retinal images is clinically significant. In this study, an automatic method to detect hard exudates is proposed. The algorithm is based on mixture models to dynamically threshold the images in order to separate exudates from background. A postprocessing technique, based on edge detection, is applied to distinguish hard exudates from cotton wool spots and other artefacts. We prospectively assessed the algorithm performance using a database of 80 retinal images with variable colour, brightness, and quality. The algorithm obtained a sensitivity of 90.2% and a positive predictive value of 96.8% using a lesion-based criterion. The image-based classification accuracy is also evaluated obtaining a sensitivity of 100% and a specificity of 90%.

Literature Review:
Hard exudates found yellow or white flecks in the outer layer of the retinal vasculature of those humans who are affected by diabetes. More generally, hard exudates are the microaneurysm that is found as white dots without blood in the lumen. Sometimes these exudates are deposited along with the vein of the retina. Hard exudates are bright interretinal protein deposition and considered as the hallmark of diabetes BIB002 . Figure 5 shows the hard exudate in the fundus retinal image. An automatic image processing technique was developed by authors of BIB003 following Fisher's Linear Discriminant Analysis with a dataset containing 58 fundus retinal images concentrating on the variables like quality, color, and brightness which gained 88% sensitivity with the false positive mean number 4.83±4.64 per image. They used performance evaluation criterion based on the lesion and obtained 100% accuracy of image-based classification BIB003 . The study of BIB004 proposed an automated image segmentation algorithm to segment the exudates in fundus retinal images from DRIVE and STARE dataset using their green component and preprocessing steps such as contrast adjustment, average filtering, thresholding, and finally they experienced the 96.7% sensitivity BIB004 . The authors of BIB001 also performed an experiment that showed the improvement of accuracy in detecting hard exudates in retinal images and they achieved 100% and 74% specificity. The authors of BIB005 detect hard exudates using the mixture model and thresholding technique to isolate the exudates from the background and then applied edge detection to differentiate exudates from CWS. This formula showed 90.2% sensitivity and 96.8% positive predictive value. The accuracy of the image-based classification was satisfactory as the 100% sensitivity and 90% specificity were obtained BIB005 .