Medical image processing and analysis Machine learning Computer-aided retinal disease diagnosis Glaucoma
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.
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.
This paper is in closed access until 12 months after it is published.
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.