Title |
A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis
|
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Published in |
Journal of Medical Systems, December 2017
|
DOI | 10.1007/s10916-017-0859-4 |
Pubmed ID | |
Authors |
Muhammad Salman Haleem, Liangxiu Han, Jano van Hemert, Baihua Li, Alan Fleming, Louis R. Pasquale, Brian J. Song |
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. |
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Geographical breakdown
Country | Count | As % |
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Unknown | 67 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 10 | 15% |
Researcher | 8 | 12% |
Student > Postgraduate | 7 | 10% |
Student > Master | 7 | 10% |
Student > Doctoral Student | 3 | 4% |
Other | 13 | 19% |
Unknown | 19 | 28% |
Readers by discipline | Count | As % |
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Computer Science | 15 | 22% |
Engineering | 5 | 7% |
Biochemistry, Genetics and Molecular Biology | 4 | 6% |
Social Sciences | 1 | 1% |
Other | 3 | 4% |
Unknown | 22 | 33% |