An Instinctive Method for Lesion Detection in Diabetic Retinopathy Images using a Novel Spatial Possibilistic C means Clustering in Kernel Space

Abstract

Diabetic Retinopathy (DR) is the prime cause of vision impediment which originates due micro vascular changes and hyperglycemia stimulated by Diabetes Mellitus (DM). Endothelial lining of the blood capillaries absorbs excess amount of glucose (glycoproteins), hence become thick but are fragile. The swollen capillaries may burst and leak water, proteins, and lipids and tends to fovea expansion. Further it triggers the revascularization to nourish retinal fundus. These new blood capillaries are weak and fragile and can further progress the state to chronic. Pupil dilation for fundus observation leads to many ill effects like head ache, brow pain, blurred vision and light sensitivity. Ophthalmologists cannot administer the pathos well if the symptoms are indolently addressed by the patient and therefore reliability during subjective diagnosis lags. This paper address a qualitative evaluation of novel possibilistic clustering methods with induced spatial constraint in kernel domain to detect the presence of exudates in non-dilated DR images. This methods are compiled in an N dimensional Kernel space which helps to easily segregate the non-linear data regions present in the lower dimensional input space. Also the inclusion of spatial information of a pixel neighborhood will improves the noise handling capability of the proposed methods by easily extricating the noisy pixels from the target lesions and hence improves the system’s accuracy in attaining reliable data. Statistical evaluation reveals that the proposed algorithms has attained better sensitivity and specificity compared to existing state-of-art works.