A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Challenges and Future Work <s> Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods. <s> BIB001 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Challenges and Future Work <s> A reliable and accurate method to measure the width of retinal blood vessel in fundus photography is proposed in this paper. Our approach is based on a graph-theoretic algorithm. The two boundaries of the same blood vessel are segmented simultaneously by converting the two-boundary segmentation problem into a two-slice, three-dimension surface segmentation problem, which is further converted into the problem of computing a minimum closed set in a node-weighted graph. An initial segmentation is generated from a vessel probability image. Two datasets from the REVIEW database were used to assess the performance of proposed method. This algorithm is robust and is able to produce accurate measurements. <s> BIB002 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Challenges and Future Work <s> In comparison with the standard three-channel colour images, spectral retinal images provide more detailed information about the structure of the retina. However, the availability of spectral retinal images for the research and development of image analysis methods is limited. In this paper, we propose two approaches to reconstruct spectral retinal images based on common RGB images. The approaches make use of fuzzy c-means clustering to perform quantization of the image data, and the radial basis function network to learn the mapping from the three-component color representation to the spectral space. The dissimilarities between the reconstructed spectral images and the original ones are evaluated on a retinal image set with spectral and RGB images, and by using a standard spectral quality metric. The experimental results show that the proposed approaches are able to reconstruct spectral retinal images with a relatively high accuracy. <s> BIB003 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Challenges and Future Work <s> Purpose: Retinal blood vessel segmentation is crucial as it is the earliest process in measuring various indicators of retinopathy sign such as arterial-venous nicking, and focal arteriolar and generalized arteriolar narrowing. The segmentation can be clinically used if its accuracy is close to 100%. In this study, a new method of segmentation is developed for extraction of retinal blood vessel. ::: Methods: In this paper, we present a new automated method to extract blood vessels in retinal fundus images. The proposed method comprises of two main parts and a few subcomponents which include pre-processing and segmentation. The main focus for the segmentation part is two morphological reconstructions which are the morphological reconstructions followed by the morphological top-hat transform. Then the technique to classify the vessel pixels and background pixels is Otsu’s thresholding. The image database used in this study is the High Resolution Fundus Image Database (HRFID). ::: Results: The developed segmentation method accuracy are 95.17%, 92.06% and 94.71% when tested on dataset of healthy, diabetic retinopathy (DR) and glaucoma patients respectively. ::: Conclusion: Overall, the performance of the proposed method is comparable with existing methods with overall accuracies were more than 90 % for all three different categories: healthy, DR and glaucoma <s> BIB004
Fundus retinal image processing has become one of the most interesting technologies in diagnosing many cardiovascular diseases such as stroke. According to the medical study, some of the key features of retinal microvasculature convey the symptom of stroke such as the existence of hard exudates, microaneurysm, CWS, and changes in the vessel diameter. To be cooperative with the physicians in terms of detecting the early signs of this lethal condition, researchers from biomedical engineering discipline are being involved more enthusiastically. Numerous quantitative methods for quantifying the abnormal changes in vessel diameter have been developed to provide more precision in medical diagnosing. But, still, there is a scarcity of automatic vascular caliber quantifying methods with more accuracy especially when dealing with images with abnormality. The existing image acquisition technique still has limitations on the basis of automation though there are some ultramodern image acquisition techniques that have been developed which are applicable in the smartphone and the misery in autoalignment of the captured image based on a standard preregistered image. One of the potential ways to avoid this penury is to apply the image registration technique prior to image acquisition or segmentation. Images can be degraded with blur at the time of acquisition and it is either space-variant or space-invariant. Maximum image acquisition algorithm is not effective in a space-invariant blur. It is known that spectral image is more informative for retinal images. Lack of spectral retinal image is also considered as an obstacle of image processing BIB003 . Edge detection is also important in the workflow of measuring blood vessel diameter as several vessel diameter measuring algorithms go through this technique. Though a lot of edge detection algorithms are available, sometimes the performance of the existing algorithms degraded due to the poor local contrast and wrong illustration of the central light reflection in fundus images BIB001 . This scenario greatly affects the results of computational technique to measure the vessel diameter especially the smaller vessel diameter measurement. Another worst condition arises in detecting edges and measuring the vessel width if false positive or vessel discontinuities occurred BIB002 . To avoid this situation edge detecting technology should be developed to a great extent. Some challenges for image registration technique as mentioned by ETDRS are the feature-based method problems in superfluous landmark points due to small overlaps between adjacent fields. The area-based technique cannot perform well because of irregular intensity distribution from defective data acquisition. Also, both feature and area-based technique can result in less accuracy due to the high-resolution images containing huge homogeneous textureless regions  . It is observed that graph-theoretic method has a large contribution in the development of the vessel boundary segmentation and vessel width measurement technique. Some drawback of the vessel width measurement algorithm based on the graph is unable to define the normal direction of the blood vessel that leads them to result in poor accuracy BIB002 . As the vessel width measurement algorithm depends on the segmentation, the inefficient segmentation method also distorts the outcome of the system. Another most important issue is that the image quality of the databases which are being used widely to validate the designed algorithm for retinal vessel width measurement. In some cases, it was found that the performance of vessel width measurement method was affected due to the different features and qualities of the images from different datasets. Supposing the diabetic abnormalities are present in the images of HRIS dataset, highly blurred vessel edges of the image of CLRIS dataset and the noisy, pathological images with lower resolution images of the VDIS dataset are found as responsible for the degradation of the performance of the proposed algorithm. For the better accuracy and to create supreme enthusiasm among the researchers the quality of the contents of the testing image sets needs to be updated. Employment of robust retinal blood vessel segmentation algorithm also overcomes the issue of image quality. The proposed method of BIB004 had shown its effectiveness on the unhealthy dataset. Thus it is believed that the method is meaningful when applying on the abnormal retinal dataset. With the rapid development of technology, retinal image processing is also marching forward with lots of novel achievements to provide the human beings with a more secured life creating a notable application that can detect the preindication of stroke with more accuracy. The latest technology of retinal image analysis is only for the image acquisition using a smartphone and that is also dependent on a large screen and power consuming devices for further processing. A fully automated image processing technique to extract the features and measure the vessel diameter of the human retinal vasculature with maximum accuracy needs to be developed. The system can be operated by using a smartphone to facilitate the regarding ophthalmologist with the most feasible and expected teleophthalmic system for the diagnosis, supervision, and monitoring diseases state of lifethreatening microvascular disease like stroke.