Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images
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- 作者：U. Rajendra Acharyaa ; b ; c ; U. Raghavendrad ; Hamido Fujitae ; Yuki Hagiwaraa ; Joel EW Koha ; Tan Jen Honga ; Vidya K. Sudarshana ; vidya.2kus@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Anushya Vijayananthanf ; Chai Hong Yeongf ; Anjan Gudigard ; Kwan Hoong Ngf
- 关键词：Cirrhosis ; Curvelet transform ; Fatty liver disease ; Probabilistic neural network ; Locality sensitive discriminant analysis ; Ultrasound
- 刊名：Computers in Biology and Medicine
- 出版年：2016
- 出版时间：1 December 2016
- 年：2016
- 卷：79
- 期：Complete
- 页码：250-258
- 全文大小：533 K
文摘
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Automated identification of normal, FLD and cirrhosis using ultrasound images.
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Curvelet Transform-based nonlinear features are extracted.
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Achieved classification accuracy of 97.33% using PNN classifier.
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Liver disease index is proposed to discriminate three classes.