Automated Classification of Fatty and Normal Liver Ultrasound Images Based on Mutual Information Feature Selection

摘要

Background: Fatty Liver Disease (FLD) is one of the most critical diseases that should be detected and cured at the earlier stage in order to decrease the mortality rate. To identify the FLD, ultrasound images have been widely used by the radiologists. However, due to poor quality of ultrasound images, they found difficulties in recognizing FLD. To resolve this problem, many researchers have developed various Computer Aided Diagnosis (CAD) systems for the classification of fatty and normal liver ultrasound images. However, the performance of existing CAD systems is not good in terms of sensitivity while classifying the FLD.Methods: In this paper, an attempt has been made to present a CAD system for the classification of liver ultrasound images. For this purpose, texture features are extracted by using seven different texture models to represent the texture of Region of Interest (ROI). Highly discriminating features are selected by using Mutual Information (MI) feature selection method.Results: Extensive experiments have been carried out with four different classifiers, and for carrying out this study, 90 liver ultrasound images have been taken. From the experimental results, it has been found that the proposed CAD system is able to give 95.55% accuracy and sensitivity of 97.77% with the 20 best features selected by the MI feature selection technique.Conclusion: The experimental results show that the proposed system can be used for the classification of fatty and normal liver ultrasound images with higher accuracy. (C) 2018 AGBM. Published by Elsevier Masson SAS. All rights reserved.