Classification of Liver Cirrhosis with Statistical Analysis of Texture Parameters

Authors

  • Hafeez Ullah Janjua The Islamia University of Bahawalpur
  • Farah Andleeb Government Sadiq College Women University, Bahawalpur
  • Sidra Aftab The Islamia University of Bahawalpur, Bahawalpur
  • Fayyaz Hussain Bahauddin Zakariya University, University Campus, Bosan Road, Multan
  • Ghulam Gilanie COMSATS Institute of Information Technology, Lahore

Abstract

Attempts have been made to study texture parameters for differentiation of the images. This paper aims at the classification of normal and abnormal (i.e. cirrhosis) ultrasound liver images through custom developed feature extractor software (FES). To classify the slices as normal and abnormal, different regions of interest (ROI) are processed to extract texture features based on statistical moments. Experiments reveal that ROI of size 64 × 64 is best for liver images classification. Statistical texture features like standard deviation, kurtosis, skewness, flatness, entropy and energy are extracted via FES. There is a significant variation in values of these seven parameters for abnormal images as compared with healthy ones. A machine learning tool, Waikato Environment Knowledge Analysis (Weka), has been used to verify the standard evaluation parameters by calculating precision, mean error, kappa statistics, ROC area, TP rate and FP rate. This tool shows an excellent agreement with our derived results. Overall, the proposed method of classification of normal and abnormal liver slices obtained accuracy of 96%. This work supports in visual identification of images using computer aided diagnostic tool that helps radiologist and doctors to diagnosing medical images automatically absolute results instantly can be found.

Author Biographies

  • Farah Andleeb, Government Sadiq College Women University, Bahawalpur
    Department of Physics
  • Sidra Aftab, The Islamia University of Bahawalpur, Bahawalpur
    Biophotonics Research Laboratory, Department of Physics
  • Fayyaz Hussain, Bahauddin Zakariya University, University Campus, Bosan Road, Multan
    Department of Physics
  • Ghulam Gilanie, COMSATS Institute of Information Technology, Lahore
    Department of Computer Science

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Published

2018-02-17

Issue

Vol. 3 No. 2 (2017)

Section

Articles