Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma. (February 2016)

• Record Type: Journal Article
• Authors: Mittapalli, Pardha Saradhi; Kande, Giri Babu
• Is Part Of: Biomedical signal processing and control. Volume 24:(2015)
• Page Start: 34
• Page End: 46
• Publication Date: 2016-02
• Subjects: Fundus image -- Glaucoma -- Optic disk -- Cup -- Active contour; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.09.003
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: A Comprehensive Glaucoma Expert System is developed to assess the severity of glaucoma. A novel blood vessel inpainting method is proposed. A new extended LBF active contours algorithm for the segmentation of OD is proposed. An algorithm based on SWFCM clustering and morphological operations for Optic Cup Segmentation is presented. Abstract: Glaucoma is an eye disease that results in irreversible loss of vision. The manual examination of optic disk (OD) is a standard procedure used for detecting glaucoma. This paper presents a glaucoma expert system based on the segmentations of OD and optic cup attained from color fundus images. A novel implicit region based active contour model is proposed for OD segmentation which incorporates the image information at the point of interest from multiple image channels to have robustness against the variations found in and around the OD region. A novel optic cup segmentation method is also proposed based on the structural and gray level properties of cup. Based on the precise information about the contours of OD and cup different parameters are calculated for glaucoma assessment. The proposed system is evaluated on 59 retinal images comprising 17 normal and 42 glaucomatous images against the groundtruths given by an experienced ophthalmologist. The proposed OD segmentation method achieved an average F -score of 0.975, average boundary distance of 10.112 pixel and average correlation coefficient of 0.916. The cup segmentation method attained an average F -score of 0.89, average boundary distance of 18.927 pixel and average correlation coefficient of 0.835. The mean error and standard deviation of the error σ for all the parameters are much smaller in glaucomatous images compared to normal images. This indicates high sensitivity of the proposed method in glaucoma assessment.