title: 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.
authors.affiliation: Department of Electrical Engineering, Indian Institute of Technology Roorkee , Roorkee, Uttarakhand , India .
authors.forename: M B
authors.initials: MB
authors.lastname: Subramanya
authors.forename: Vinod
authors.initials: V
authors.lastname: Kumar
authors.forename: Shaktidev
authors.initials: S
authors.lastname: Mukherjee
authors.forename: Manju
authors.initials: M
authors.lastname: Saini
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005234:Fatty Liver / Q000000981:diagnostic imaging*; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging*; D060388:Support Vector Machine; D014463:Ultrasonography
keywords: Classification; differential evolution feature selection; fatty liver disease; support vector machine; texture features