30 October 2018 Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma
Rashmi Panda, Niladri B. Puhan, Aparna Rao, Bappaditya Mandal, Debananda Padhy, Ganapati Panda
Author Affiliations +
Abstract
Glaucoma is a progressive optic neuropathy characterized by peripheral visual field loss, which is caused by degeneration of retinal nerve fibers. The peripheral vision loss due to glaucoma is asymptomatic. If not detected and treated at an early stage, it leads to complete blindness, which is irreversible in nature. The retinal nerve fiber layer defect (RNFLD) provides an earliest objective evidence of glaucoma. In this regard, we explore cost-effective redfree fundus imaging for RNFLD detection to be practically useful for computer-assisted early glaucoma risk assessment. RNFLD appears as a wedge shaped arcuate structure radiating from the optic disc. The very low contrast between RNFLD and background makes its visual detection quite challenging even by medical experts. In our study, we formulate a deep convolutional neural network (CNN) based patch classification strategy for RNFLD boundary localization. A large number of RNFLD and background image patches train the deep CNN model, which extracts sufficient discriminative information from the patches and results in accurate RNFLD boundary pixel classification. The proposed approach is found to achieve enhanced RNFLD detection performance with sensitivity of 0.8205 and false positive per image of 0.2000 on a newly created early glaucomatic fundus image database.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2018/$25.00 © 2018 SPIE
Rashmi Panda, Niladri B. Puhan, Aparna Rao, Bappaditya Mandal, Debananda Padhy, and Ganapati Panda "Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma," Journal of Medical Imaging 5(4), 044003 (30 October 2018). https://doi.org/10.1117/1.JMI.5.4.044003
Received: 7 May 2018; Accepted: 2 October 2018; Published: 30 October 2018
Lens.org Logo
CITATIONS
Cited by 20 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Nerve

Neural networks

Image enhancement

Defect detection

Image classification

Databases

Visualization

Back to Top