ICTACT Journal on Image and Video Processing

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Improved Automatic Detection of Glaucoma using Cup-To-Disk Ratio and Hybrid Classifiers


Affiliations
1 Department of Computer Science and Engineering, BNM Institute of Technology, India
2 University Visvesvaraya College of Engineering, Bangalore University, India
     

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Glaucoma is one of the most complicated disorder in human eye that causes permanent vision loss gradually if not detect in early stage. It can damage the optic nerve without any symptoms and warnings. Different automated glaucoma detection systems were developed for analyzing glaucoma at early stage but lacked good accuracy of detection. This paper proposes a novel automated glaucoma detection system which effectively process with digital colour fundus images using hybrid classifiers. The proposed system concentrates on both Cup-to Disk Ratio (CDR) and different features to improve the accuracy of glaucoma. Morphological Hough Transform Algorithm (MHTA) is designed for optic disc segmentation. Intensity based elliptic curve method is used for separation of optic cup effectively. Further feature extraction and CDR value can be estimated. Finally, classification is performed with combination of Naive Bayes Classifier and K Nearest Neighbour (KNN). The proposed system is evaluated by using High Resolution Fundus (HRF) database which outperforms the earlier methods in literature in various performance metrics.

Keywords

Glaucoma, Optic Nerve, Cup-To-Disc Ratio, HRF Database, Hybrid Classifier.
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  • Improved Automatic Detection of Glaucoma using Cup-To-Disk Ratio and Hybrid Classifiers

Abstract Views: 183  |  PDF Views: 3

Authors

Deepthi K. Prasad
Department of Computer Science and Engineering, BNM Institute of Technology, India
L. Vibha
Department of Computer Science and Engineering, BNM Institute of Technology, India
K. R. Venugopal
University Visvesvaraya College of Engineering, Bangalore University, India

Abstract


Glaucoma is one of the most complicated disorder in human eye that causes permanent vision loss gradually if not detect in early stage. It can damage the optic nerve without any symptoms and warnings. Different automated glaucoma detection systems were developed for analyzing glaucoma at early stage but lacked good accuracy of detection. This paper proposes a novel automated glaucoma detection system which effectively process with digital colour fundus images using hybrid classifiers. The proposed system concentrates on both Cup-to Disk Ratio (CDR) and different features to improve the accuracy of glaucoma. Morphological Hough Transform Algorithm (MHTA) is designed for optic disc segmentation. Intensity based elliptic curve method is used for separation of optic cup effectively. Further feature extraction and CDR value can be estimated. Finally, classification is performed with combination of Naive Bayes Classifier and K Nearest Neighbour (KNN). The proposed system is evaluated by using High Resolution Fundus (HRF) database which outperforms the earlier methods in literature in various performance metrics.

Keywords


Glaucoma, Optic Nerve, Cup-To-Disc Ratio, HRF Database, Hybrid Classifier.

References