Optic nerve head segmentation using fundus images and optical coherence tomography images for glaucoma detection

• Published in: Biomedical papers of the Medical Faculty of the University Palacký, Olomouc, Czechoslovakia Vol. 159; no. 4; pp. 607 - 615
• Main Authors: Ganesh Babu, T R, Shenbaga Devi, S, Venkatesh, R
• Format: Journal Article
• Language: English
• Published: Czech Republic Palacký University Olomouc, Faculty of Medicine and Dentistry 01.12.2015
• Subjects: blood vessels; cup depth; Fluorescein Angiography - methods; glaucoma; Glaucoma - diagnostic imaging; Glaucoma - pathology; Humans; Multimodal Imaging - methods; neuro retinal rim; optic cup; optic disc; Optic Disk - diagnostic imaging; Optic Disk - pathology; Retina - diagnostic imaging; Retina - pathology; retinal thickness; Sensitivity and Specificity; Tomography, Optical Coherence - methods; glaucoma; cup depth; blood vessels; retinal thickness; optic cup; optic disc; neuro retinal rim
• ISSN: 1213-8118; 1804-7521
• DOI: 10.5507/bp.2015.053

Glaucoma is a common causes of blindness. The associated elevation in intra ocular pressure leads to progressive degeneration of the optic nerve and resultant structural changes with functional failure of the visual field. Since, glaucoma is asymptomatic in the early stages and the associated vision loss is irreparable, its early detection and timely medical treatment is essential to prevent further visual damage. This paper presents a novel method for glaucoma detection using digital fundus image and optical coherence tomography (OCT) image. The first section focuses on the features such as cup to disc ratio (CDR) and the inferior superior nasal temporal (ISNT) ratio which were obtained from fundus images.The above features were used for classifying the normal and glaucoma condition using back propagation neural network (BPN) and Support Vector Machine (SVM) classifiers. In the second part of the article, features such as CDR and two novel features, cup depth and retinal thickness were obtained from the OCT image. These features were evaluated by the BPN and SVM classifier. The combined features from fundus and OCT images were analyzed. The system proposed here is able to classify glaucoma automatically. The accuracy of BPN and SVM Classifiers was 90.76% and 96.92% respectively.