A Composite Feature Set Based Blood Vessel Segmentation in Retinal Images through Supervised Learning

Authors:

Y. Madhu Sudhana Reddy,R. S. Ernest Ravindran,

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00009

Keywords:

retinal vessel segmentation,Gabor filter,Support vector machine,Gradient features,Correlation Accuracy,

Abstract

Retinal image analysis has gained a significant research interest due to its widespread applicability in the diagnosis of different eye related diseases. This paper focused in the analysis of Diabetic Retinopathy through different features (Optic Disk, Retinal Vessels, and Exudates etc.,) of retinal image. Towards this objective, a new Retinal Vessel Segmentation mechanism is introduced in this paper. The proposed mechanism accomplished the Gabor Filter for Feature Extraction and Support Vector Machine Algorithm for classification. Here the Gabor Filter ensures a more resilience to the scaling and orientation issues in the retinal image. Afterwards, a feature set consists of thirteen features is extracted from retinal image to provide a proper differentiation between the image pixels and background pixels. Based on these features, the SVM classifier classifies the vessel pixels and background pixels more effectively which improves the classification accuracy and reduces false positive rate. An extensive simulation carried out over the proposed approach through two standard datasets, DRIVE and STARE reveals the outstanding performance with respect to the performance metrics sensitivity, specificity and accuracy.

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