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Hidden Markov Random Field and Gaussian Mixture Model Based Hidden Markov Random Field for Contour Labelling of Exudates in Diabetic Retinopathy—A Comparative Study

  • E. Revathi Achan
  • T. R. Swapna
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Diabetic Retinopathy (DR) is one of the important causes of blindness in diabetic patients. Diabetes that affects the retina is called diabetic retinopathy. Diabetic retinopathy occurs due to the damage of blood vessels in retina and increase in the level of glucose. Different pathologies are normally seen in DR such as microaneurysms, hard exudates, soft exudates, cotton wool spots and haemorrhages. We have done a comparative study of Hidden Markov Random Field (HMRF) and Gaussian Mixture Model (GMM) based HMRF for automatic segmentation of exudates and the performance analysis of both methods. The preprocessing consists of candidate extraction step using greyscale morphological operation of closing and initial labelling of exudates using K-means clustering followed by contour detection. In contour detection, we have analysed two approaches, one is GMM-based HMRF and the other is HMRF. DIARETDB1 is the dataset used.

Keywords

Diabetic retinopathy Colour fundus Exudates Hidden Markov random field Gaussian mixture model 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia

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