The Community for Technology Leaders
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2016)
Shenzhen, China
Dec. 15, 2016 to Dec. 18, 2016
ISBN: 978-1-5090-1612-9
pp: 799-804
Shuo Hong Wang , School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, China
Xiang Liu , School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, China
Jingwen Zhao , School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, China
Jia Lin Song , Department of ultrasound, Changzheng Hospital Affiliated to Second Military Medical University, Shanghai, China
Jian Quan Zhang , Department of ultrasound, Changzheng Hospital Affiliated to Second Military Medical University, Shanghai, China
Yan Qiu Chen , School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, China
ABSTRACT
This paper proposes a novel cirrhosis diagnosis method using high-frequency ultrasound imaging that is able to not only diagnose cirrhosis, but also determine its stage. We propose combined features extracted from both liver capsule and parenchyma texture to avoid the bias caused by considering only one aspect. The liver capsule is localized using a multi-scale, multi-objective optimization method and indices are proposed to measure the smoothness and continuity of the capsule. The parenchyma texture is modeled with Gaussian mixture model (GMM), and the lesions in the parenchyma are detected by a scale-space defect detection algorithm. The degree of pathological changes of the liver is quantitatively evaluated by 7 features describing morphology of the capsule and lesions in the parenchyma. Then SVM classifiers are trained to classify the samples into different cirrhosis stages. Experiment results demonstrate the effectiveness of the proposed method, which outperforms other 4 state-of-the-art methods and the proposed method that solely uses capsule or parenchyma texture features.
INDEX TERMS
Liver, Ultrasonic imaging, Feature extraction, Lesions, Reliability, Gaussian mixture model
CITATION

Shuo Hong Wang, Xiang Liu, Jingwen Zhao, Jia Lin Song, Jian Quan Zhang and Yan Qiu Chen, "Learning to diagnose cirrhosis via combined liver capsule and parenchyma ultrasound image features," 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China, 2016, pp. 799-804.
doi:10.1109/BIBM.2016.7822627
96 ms
(Ver 3.3 (11022016))