Automated retinal nerve fiber layer defect detection using fundus imaging in glaucoma. (June 2018)

• Record Type: Journal Article
• Authors: Panda, Rashmi; Puhan, N.B.; Rao, Aparna; Padhy, Debananda; Panda, Ganapati
• Is Part Of: Computerized medical imaging and graphics. Volume 66(2018)
• Page Start: 56
• Page End: 65
• Publication Date: 2018-06
• Subjects: Glaucoma -- Fundus image -- Retinal nerve fiber layer (RNFL) -- Patch features -- Random forest; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2018.02.006
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: We present a novel automatic retinal nerve fiver layer defect (RNFLD) detection in redfree fundus images. Two new features are introduced to describe RNFLD patches. RNFLD boundary pixels are classified by patch features driven random forest. Angular width of RNFLD is computed which can be used as a treatment follow up. Experimental results show that our method can accurately detect RNFLDs. Abstract: Retinal nerve fiber layer defect (RNFLD) provides an early objective evidence of structural changes in glaucoma. RNFLD detection is currently carried out using imaging modalities like OCT and GDx which are expensive for routine practice. In this regard, we propose a novel automatic method for RNFLD detection and angular width quantification using cost effective redfree fundus images to be practically useful for computer-assisted glaucoma risk assessment. After blood vessel inpainting and CLAHE based contrast enhancement, the initial boundary pixels are identified by local minima analysis of the 1-D intensity profiles on concentric circles. The true boundary pixels are classified using random forest trained by newly proposed cumulative zero count local binary pattern ( CZC-LBP ) and directional differential energy ( DDE ) along with Shannon, Tsallis entropy and intensity features. Finally, the RNFLD angular width is obtained by random sample consensus (RANSAC) line fitting on the detected set of boundary pixels. The proposed method is found to achieve high RNFLD detection performance on a newly created dataset with sensitivity (SN) of 0.7821 at 0.2727 false positives per image (FPI) and the area under curve (AUC) value is obtained as 0.8733.