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 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.
journal_name
J Med Systjournal_title
Journal of medical systemsauthors
Haleem MS,Han L,Hemert JV,Li B,Fleming A,Pasquale LR,Song BJdoi
10.1007/s10916-017-0859-4subject
Has Abstractpub_date
2017-12-07 00:00:00pages
20issue
1eissn
0148-5598issn
1573-689Xpii
10.1007/s10916-017-0859-4journal_volume
42pub_type
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