Next Article in Journal
Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents
Previous Article in Journal
Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(1), 149;

Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification

School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 201203, China
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
Department of ultrasound, Changzheng Hospital Affiliated to Second Military Medical University, Shanghai 200003, China
Author to whom correspondence should be addressed.
Academic Editor: Xiaoning Jiang
Received: 20 November 2016 / Revised: 27 December 2016 / Accepted: 10 January 2017 / Published: 13 January 2017
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [2911 KB, uploaded 13 January 2017]   |  


This paper proposes a computer-aided cirrhosis diagnosis system to diagnose cirrhosis based on ultrasound images. We first propose a method to extract a liver capsule on an ultrasound image, then, based on the extracted liver capsule, we fine-tune a deep convolutional neural network (CNN) model to extract features from the image patches cropped around the liver capsules. Finally, a trained support vector machine (SVM) classifier is applied to classify the sample into normal or abnormal cases. Experimental results show that the proposed method can effectively extract the liver capsules and accurately classify the ultrasound images. View Full-Text
Keywords: ultrasound imaging; computer-aided diagnosis; cirrhosis; convolutional neural network ultrasound imaging; computer-aided diagnosis; cirrhosis; convolutional neural network

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Liu, X.; Song, J.L.; Wang, S.H.; Zhao, J.W.; Chen, Y.Q. Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification. Sensors 2017, 17, 149.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top