alexa Retinal Microaneurysm Exclusion on Optic Disc and Detection Using Cross Section Profile Analysis
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

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Special Issue Article

Retinal Microaneurysm Exclusion on Optic Disc and Detection Using Cross Section Profile Analysis

K.Ruthra
PG Student (CSE), Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India
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Abstract

Diabetic retinopathy (DR) is one of the complications of diabetes that develops in most of the patients with long- standing illness and the leading cause of blindness in the developed countries. Though effective treatments for DR are available it requires early diagnosis and the continuous monitoring of diabetic patients. Diagnosis of DR is performed by the evaluation of retinal (fundus) images. Manual grading of these images to determine the severity of DR is rather slow and resource demanding. The presence of microaneurysms (MA's) on the retina is the first and most characteristic symptom of this disease .MA's on the retina appear as small, round shaped, red dots. No additional vessel or optic disc detection step is applied in existing system. The proposed method proved to be able to distinguish vessel bifurcations and crossings from MA's rather well however, some of the false positives come from the region of the optic disc. Since the contrast is very high in the region of optic disc, sometimes a rather high score is assigned. Though the existing method showed convincing performance, this could probably be further improved by adding an optic disc detection step and excluding MA detections within this region. The proposed method realizes MA detection through the analysis of directional cross-section profile centered on the local maximum pixels of the pre-processed image. Peak detection is applied on each profile and a set of attributes regarding the size, height, and shape of the peak are calculated subsequently. The statistical measures of these attribute values as the orientation of the cross-section changes constitute the feature set that is used in a naive bayes classification to exclude spurious candidates. A formula is given for the final score of the remaining candidates, which can be threshold further for a binary output. The proposed method has been tested in the retinopathy online challenge, where it proved to be competitive with the stateof- the-art method.

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