Retinal Blood Vessel Segmentation from Depigmented Diabetic Retinopathy Images

Abstract

Diabetic Retinopathy is a progressive disease that affects diabetic patients and changes the width and tortuosity of the retinal blood vessels. The preferred center of attention is to predict the new vessel growth and the dissimilarity in diameter of the retinal blood vessels. To examine the changes, primarily segmentation has to be made. A system has been proposed to enhance the quality of the segmentation result over pathological retinal images. The proposed system comprises preprocessing of Fundus images and extracts the blood vessels. The proposed system uses Contrast Limited Adaptive Histogram Equalization (CLAHE) for preprocessing and Tandem Pulse Coupled Neural Network (TPCNN) model to segment the retinal vasculature. To categorize the small blood vessels from pathological images, the algorithm depending on its parameters. In the former PCNN model, the parameters have to be set at every time for all images. The proposed TPCNN model assigns values for its multiple parameters through Particle Swarm Optimization (PSO); so that the decay speeds of the threshold would be regulated adaptively. This greatly enhances the flexibility of TPCNN in dealing with depigmented pathological images. The generated feature vectors of blood vessels are classified and extracted via Deep Learning Based Support Vector Machine (DLBSVM) technique. The proposed method is assessed over DRIVE, STARE, HRF, REVIEW, CHASE_DB1 and DRIONS databases by the performance parameters such as Sensitivity, Specificity, Accuracy, and Receiver Operating Characteristic (ROC) curve. The results render that these techniques improve the segmentation with an average value of 94.68% Sensitivity, 99.70% Specificity, 99.61% Accuracy and 98% ROC. The results evoke that the proposed methods are a suitable alternative for the supervised methods.

KEYWORDS:

• Diabetic retinopathy
• Fundus image
• Neural network
• Ophthalmology
• Retinal blood vessel

ACKNOWLEDGMENT

The authors would like to thank the subjective experts Dr. J. Kishore Kumar Jacob and Dr. H. Hector for providing a subjective analysis of the segmentation results.

Additional information

Notes on contributors

T. Jemima Jebaseeli

T Jemima Jebaseeli is currently working as an assistant professor in Karunya Institute of Technology and Sciences, Coimbatore. Her research interests include medical Image Processing, image fusion, and image compression. She has published various papers in reputed international journals and conferences. Corresponding author. Email: [email protected].

C. Anand Deva Durai

C Anand Deva Durai has completed his doctor of philosophy in computer science and engineering from Manonmaniam Sundaranar University, Tirunelveli and presently working as an assistant professor in Department of CSE, King Khalid University, Abha, Saudi Arabia. His primary research interests lie in pattern recognition, biometrics, computer vision, artificial intelligence, and intelligent systems. He is a life time member of the Computer Society of India. Email: [email protected]

J. Dinesh Peter

J Dinesh Peter is currently working as associate professor, Department of Computer Sciences Technology at Karunya Institute of Technology and Sciences, Coimbatore. Prior to this, he was a full time research scholar at National Institute of Technology, Calicut, India, from where he received his PhD in computer science and engineering. His research focus includes big-data, image processing, and computer vision. He has several publications in various reputed international journals and conference papers which are widely referred to. He is a member of IEEE, MICCAI, Computer Society of India and Institution of Engineers India. He has served as session chairs and delivered plenary speeches for various international conferences and workshops. Also, he has conducted many international conferences and been as editor for Springer proceedings and many special issues in journals. Email: [email protected]