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Staging of Fatty Liver Diseases Based on Hierarchical Classification and Feature Fusion for Back-Scan-Converted Ultrasound Images.

Fatty liver disease is progressive and may not cause any symptoms at early stages. This disease is potentially fatal and can cause liver cancer in severe stages. Therefore, diagnosing and staging fatty liver disease in early stages is necessary. In this paper, a novel method is presented to classify...

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Published in:Ultrasonic Imaging Vol. 39; no. 2; pp. 79 - 96
Main Authors: Owjimehr, Mehri, Danyali, Habibollah, Helfroush, Mohammad Sadegh, Shakibafard, Alireza
Format: research Journal Article
Published: Sage Publications Inc. Mar2017
Online Access:View this record in EBSCOhost
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      dt: Mar2017
      vid: 39
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        atl: Staging of Fatty Liver Diseases Based on Hierarchical Classification and Feature Fusion for Back-Scan-Converted Ultrasound Images.
      aug:
        au:
          Owjimehr, Mehri
          Danyali, Habibollah
          Helfroush, Mohammad Sadegh
          Shakibafard, Alireza
        affil: 1 Shiraz University of Technology, Shiraz, Iran
      sug:
        subj:
          Fatty Liver Classification
          Fatty Liver
          Ultrasonography Methods
          Sensitivity and Specificity
          Algorithms
          Liver Cirrhosis
          Signal Processing, Computer Assisted
          Image Enhancement Methods
          Image Interpretation, Computer Assisted Methods
          Human
      ab: Fatty liver disease is progressive and may not cause any symptoms at early stages. This disease is potentially fatal and can cause liver cancer in severe stages. Therefore, diagnosing and staging fatty liver disease in early stages is necessary. In this paper, a novel method is presented to classify normal and fatty liver, as well as discriminate three stages of fatty liver in ultrasound images. This study is performed with 129 subjects including 28 normal, 47 steatosis, 42 fibrosis, and 12 cirrhosis images. The proposed approach uses back-scan conversion of ultrasound sector images and is based on a hierarchical classification. The proposed algorithm is performed in two parts. The first part selects the optimum regions of interest from the focal zone of the back-scan-converted ultrasound images. In the second part, discrimination between normal and fatty liver is performed and then steatosis, fibrosis, and cirrhosis are classified in a hierarchical basis. The wavelet packet transform and gray-level co-occurrence matrix are used to obtain a number of statistical features. A support vector machine classifier is used to discriminate between normal and fatty liver, and stage fatty cases. The results of the proposed scheme clearly illustrate the efficiency of this system with overall accuracy of 94.91% and also specificity of more than 90%.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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