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Proceedings Paper

Support vector machine based IS/OS disruption detection from SD-OCT images
Author(s): Liyun Wang; Weifang Zhu; Jianping Liao; Dehui Xiang; Chao Jin; Haoyu Chen; Xinjian Chen
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Paper Abstract

In this paper, we sought to find a method to detect the Inner Segment /Outer Segment (IS/OS)disruption region automatically. A novel support vector machine (SVM) based method was proposed for IS/OS disruption detection. The method includes two parts: training and testing. During the training phase, 7 features from the region around the fovea are calculated. Support vector machine (SVM) is utilized as the classification method. In the testing phase, the training model derived is utilized to classify the disruption and non-disruption region of the IS/OS, and calculate the accuracy separately. The proposed method was tested on 9 patients' SD-OCT images using leave-one-out strategy. The preliminary results demonstrated the feasibility and efficiency of the proposed method.

Paper Details

Date Published: 21 March 2014
PDF: 6 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341U (21 March 2014); doi: 10.1117/12.2043439
Show Author Affiliations
Liyun Wang, Soochow Univ. (China)
Weifang Zhu, Soochow Univ. (China)
Jianping Liao, Joint Shantou International Eye Ctr. (China)
Dehui Xiang, Soochow Univ. (China)
Chao Jin, Soochow Univ. (China)
Haoyu Chen, Joint Shantou International Eye Ctr. (China)
Xinjian Chen, Soochow Univ. (China)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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