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Automated Classification of Liver Disorders using Ultrasound Images

Journal of Medical Systems volume 36, pages 3163–3172 (2012)Cite this article

Abstract

This paper presents a novel approach for detection of Fatty liver disease (FLD) and Heterogeneous liver using textural analysis of liver ultrasound images. The proposed system is able to automatically assign a representative region of interest (ROI) in a liver ultrasound which is subsequently used for diagnosis. This ROI is analyzed using Wavelet Packet Transform (WPT) and a number of statistical features are obtained. A multi-class linear support vector machine (SVM) is then used for classification. The proposed system gives an overall accuracy of ~95% which clearly illustrates the efficacy of the system.

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Conflict of interest

The authors do not have any conflict of interest.

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Affiliations

  1. Department of Computer Science, Colorado State University, Fort Collins, CO, 80523, USA

    Fayyaz ul Amir Afsar Minhas

  2. Multan Institute of Nuclear Medicine and Radiotherapy (MINAR), Multan, Pakistan

    Durre Sabih

  3. Department of Computer Science, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan

    Mutawarra Hussain

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  1. Fayyaz ul Amir Afsar Minhas
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  2. Durre Sabih
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  3. Mutawarra Hussain
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Correspondence to Fayyaz ul Amir Afsar Minhas.

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Minhas, F.u.A.A., Sabih, D. & Hussain, M. Automated Classification of Liver Disorders using Ultrasound Images. J Med Syst 36, 3163–3172 (2012). https://doi.org/10.1007/s10916-011-9803-1

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  • DOI: https://doi.org/10.1007/s10916-011-9803-1

Keywords

  • Ultrasound
  • Fatty liver disease
  • Heterogeneous liver
  • Wavelet packet transform
  • Support vector machines