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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/27549

Title: A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis
Authors: Haleem, Muhammad Salman
Han, Liangxiu
van Hemert, Jano
Li, Baihua
Fleming, Alan
Pasquale, Louis R.
Song, Brian J.
Keywords: Medical image processing and analysis
Machine learning
Computer-aided retinal disease diagnosis
Glaucoma
Issue Date: 2017
Publisher: Springer
Citation: HALEEM, M.S. ...et al., 2017. A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis. Journal of Medical Systems, In Press.
Abstract: This paper proposes a novel Adaptive Region- based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classification Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular points) based on iterative force field calculations with contours obtained from the RCM model by minimising energy function (an approach that does not require predefined geometric templates to guide auto-segmentation). Such an approach provides robustness in capturing a range of variations and shapes. We have conducted a comprehensive comparison between our approach and the state-of-the-art existing deformable models and validated it with publicly available datasets. The experimental evaluation shows that the proposed approach significantly outperforms existing methods. The generality of the proposed approach will enable segmentation and detection of other object boundaries and provide added value in the field of medical image processing and analysis.
Description: This paper is in closed access until 12 months after it is published.
Sponsor: This project is fully sponsored by EPSRC-DHPA and Optos plc., entitled "Automatic Detection of Features in Retinal Imaging to Improve Diagnosis of Eye Diseases" (Grant Ref: EP/J50063X/1). Dr. Pasquale is supported by the Harvard Glaucoma Center of Excellence. Brian J. Song has been supported by the Harvard Vision Clinical Scientist Development Program 2K12 EY016335-11.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/27549
Publisher Link: https://link.springer.com/journal/10916
ISSN: 0148-5598
Appears in Collections:Closed Access (Computer Science)

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