Diabetic Retinopathy Literature Review
Chapter 2
Literature Survey
Diabetic retinopathy is a serious disease which results from complications that arises in case of the diabetic patient. A device called fundus camera is used to record image of the patient's eye. With the help of the fundus image the trained professionals look for the signs of DR i.e. microaneurysms. It is not a easy job to do and it requires a lot of expertise and man hour to get a accurate result. Since the screening of the DR is a time consuming process and as the number of trained professionals required for this job is very low it becomes even more difficult for the patients to get the result of the screening at a timely manner. The whole process also becomes a burden for the trained professional as the number patient is very large. Therefore for a very long time the need for a automated process which can detect the microaneuryms correctly is felt.
The research in this area has been done for a long time. A different variety of techniques have been used to detect the different features of the eye i.e. blood vessels, optic disk, fovea, lesions such as exudates, microanueryms etc.
In this chapter we discuss about the different techniques used to detect these features
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[6] used segmentation to group the image into regions with same property or characteristics. Methods of image segmentation include simple thresholding, K-means Algorithm and Fuzzy C-means. Microaneurysms are distinguished from nonmicroaneurysms by a set of decision criteria which includes pixel counts, ratio of minor to major axes, robustness test, length test and holes test. The region within the range of MinPixelCount is treated as MA. A region less than the range ofMinPixelCount is treated as a background noise while a region greater than the range of MinPixelCount is treated as a non-microaneurysm. The ratio of the length of the minor axes to the major axes is determined for a candidate lesion. In diagnosing MAs, a threshold is set for this
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