Prediction of liver cirrhosis based on multiresolution texture descriptors from B-mode ultrasound

Abstract

A computer aided diagnostic system to characterise normal and cirrhotic liver by multiresolution texture descriptors is proposed in this paper. The study is carried out in 120 segmented regions of interest extracted from 31 clinically acquired B-mode liver ultrasound images. Mean and standard deviation multiresolution texture descriptors derived by using 2D-discrete wavelet transform, 2D-wavelet packet transform and 2D-Gabor wavelet transform are considered for analysis and exhaustive search with J3 criterion of class separability is used for feature selection. The performance of subset of five most discriminative texture descriptors obtained from 2D-discrete wavelet transform, 2D-wavelet packet transform and 2D-Gabor wavelet transform is compared by using a support vector machine classifier. It is observed that only five mean multiresolution texture descriptors obtained from 2D-Gabor wavelet transform at selective scale and orientations provide highest classification accuracy of 98.33% and sensitivity of 100% by using a support vector machine classifier. The promising results indicate that the selective frequency and orientation properties of Gabor filters are extremely useful for providing multiscale texture description.