Detection of Neovascularization in Proliferative
Diabetic Retinopathy Fundus Images
Suma Gandhimathi1 and Kavitha Pillai2
1Department of Computer Science
and Engineering, Sree
Vidyanikethan Engineering College, India
2Department of Computer Science
and Engineering, University
College of Engineering, India
Abstract:
Neovascularization
is a serious visual consequence disease arising from Proliferative Diabetic
Retinopathy (PDR). The condition causes progressive retinal damage in persons
suffering from Diabetes mellitus, and is characterized by busted growth of
abnormal blood vessels from the normal vasculature, which hampers proper blood
flow into the retina because of oxygen insufficiency in retinal capillaries. The present paper aims at
detecting PDR neovascularization with the help of the Adaptive Histogram
Equalization technique, which enhances the green plane of the fundus image,
resulting in enrichment of the details presented in the fundus image. The neovascularization blood vessels and the normal blood vessels
were both segmented from the equalized image, using the Fuzzy C-means
clustering technique. Marking of the neovascularization region, was achieved with a function matrix box based on a compactness classifier, which applied morphological and threshold
techniques on the segmented image. Subsequently, the Feed Forward
Back-propagation Neural Network interacted with extracted features (e.g.,
number of segments, gradient variation, mean, variance, standard deviation,
contrast, correlation, entropy, energy, homogeneity, cluster shade towards the
neovascularization detection region), in an attempt to achieve accurate
identification. The above method was tested on images from three online datasets, as well as two hospital eye
clinics. The performance of the detection technique was evaluated on these five
image sources, and found to show an overall accuracy of 94.5% for sensitivity
of 95.4% and of specificity 49.3% respectively, thus reiterating that the method would play a vital
role in the study and analysis of Diabetic Retinopathy.
Keywords: Diabetic retinopathy, neovascularization, fuzzy C-means clustering, compactness
classifier, feature extraction, neural network.