Support vector machine based IS/OS disruption detection from SD-OCT images

Liyun Wang; Weifang Zhu; Jianping Liao; Dehui Xiang; Chao Jin; Haoyu Chen; Xinjian Chen

doi:10.1117/12.2043439

21 March 2014 Support vector machine based IS/OS disruption detection from SD-OCT images

Liyun Wang, Weifang Zhu, Jianping Liao, Dehui Xiang, Chao Jin, Haoyu Chen, Xinjian Chen

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90341U (2014) https://doi.org/10.1117/12.2043439
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

Citation Download Citation

Liyun Wang, Weifang Zhu, Jianping Liao, Dehui Xiang, Chao Jin, Haoyu Chen, and Xinjian Chen "Support vector machine based IS/OS disruption detection from SD-OCT images", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341U (21 March 2014); https://doi.org/10.1117/12.2043439

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