Automated anterior segment OCT image analysis for Angle Closure Glaucoma mechanisms classification
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- 作者：Swamidoss Issac Niwasa ; issacniwas@ntu.edu.sg" class="auth_mail" title="E-mail the corresponding author ; Weisi Lina ; wslin@ntu.edu.sg" class="auth_mail" title="E-mail the corresponding author ; Xiaolong Baib ; c ; mebaixl@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Chee Keong Kwoha ; asckkwoh@ntu.edu.sg" class="auth_mail" title="E-mail the corresponding author ; C.-C. Jay Kuod ; cckuo@sipi.usc.edu" class="auth_mail" title="E-mail the corresponding author ; Chelvin C. Sngf ; chelvin@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Maria Cecilia Aquinoe ; mcdaquino@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Paul T.K. Chewf ; ophchewp@nus.edu.sg" class="auth_mail" title="E-mail the corresponding author
- 关键词：Angle closure glaucoma ; Compound image transforms ; Feature selection ; Segmentation-free method ; Machine learning classifier
- 刊名：Computer Methods and Programs in Biomedicine
- 出版年：2016
- 出版时间：July 2016
- 年：2016
- 卷：130
- 期：Complete
- 页码：65-75
- 全文大小：2035 K
文摘
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A new method for automated classification of various mechanisms of angle closure disease.
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Compound image transform features are directly extracted from the raw AS-OCT images without any segmentation.
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Anterior chamber area measurement is not required in this method.
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Selected morphological features are used to achieve better classification result.