A CAD system for B-mode fatty liver ultrasound images using texture features

Abstract

The present study proposes a computer-aided diagnosis (CAD) system for the diagnosis of grades of fatty liver disease, namely mild, moderate and severe fatty liver along with normal liver tissue. Fifty-three B-mode ultrasound images consisting of 12 normal, 14 mild, 14 moderate and 13 severe fatty liver images are used. Based on the visual interpretations by the radiologists, region of interests (ROIs) from within the liver and one ROI from the diaphragm region are considered from each image. The texture features of these ROIs are combined in three ways to form ratio features, inverse ratio features and additive features. The sub-sets of optimal features are obtained by a differential evolution feature selection (DEFS) algorithm and a support vector machine (SVM) has been used for the classification task. The Laws ratio features have shown better performance with an average accuracy and standard deviation of 84.9 ± 3.2. Hence, the CAD system could be useful to the radiologists in diagnosing grades of fatty liver disease.

• Classification
• differential evolution feature selection
• fatty liver disease
• support vector machine
• texture features

Acknowledgements

M. B. Subramanya would like to thank Dr Jitendra Virmani for valuable suggestions in improving the manuscript and the Ministry of Human Resource and Development (MHRD), India for the scholarship. The authors would like to acknowledge the Department of Electrical Engineering, Indian Institute of Technology, Roorkee and the Department of Radiology, Himalayan Institute of Hospital and Trust for their support during the period of this research work.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.