Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features

Acharya, U. Rajendra and Raghavendra, U. and Koh, Joel En Wei and Meiburger, Kristen Mariko and Ciaccio, Edward J. and Hagiwara, Yuki and Molinari, Filippo and Wai, Ling Leong and Vijayananthan, Anushya and Yaakup, Nur Adura and Mohd Fabell, Mohd Kamil and Chai, Hong Yeong (2018) Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features. Computer Methods and Programs in Biomedicine, 166. pp. 91-98. ISSN 0169-2607

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Official URL: https://doi.org/10.1016/j.cmpb.2018.10.006

Abstract

Background and objective: Liver fibrosis is a type of chronic liver injury that is characterized by an excessive deposition of extracellular matrix protein. Early detection of liver fibrosis may prevent further growth toward liver cirrhosis and hepatocellular carcinoma. In the past, the only method to assess liver fibrosis was through biopsy, but this examination is invasive, expensive, prone to sampling errors, and may cause complications such as bleeding. Ultrasound-based elastography is a promising tool to measure tissue elasticity in real time; however, this technology requires an upgrade of the ultrasound system and software. In this study, a novel computer-aided diagnosis tool is proposed to automatically detect and classify the various stages of liver fibrosis based upon conventional B-mode ultrasound images. Methods: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis. Results: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis. Conclusions: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.

Item Type: Article
Uncontrolled Keywords: Computer-aided diagnosis; Liver fibrosis; Contourlet transform; Texture features; Probabilistic neural network
Subjects: R Medicine
Divisions: Faculty of Medicine
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 08 Oct 2019 07:06
Last Modified: 08 Oct 2019 07:06
URI: http://eprints.um.edu.my/id/eprint/22710

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