Application of Texture Features for Classification of Primary Benign and Primary Malignant Focal Liver Lesions

The present work focuses on the aspect of textural variations exhibited by primary benign and primary malignant focal liver lesions. For capturing these textural variations of benign and malignant liver lesions, texture features are computed using statistical methods, signal processing based methods and transform domain methods. As an application of texture description in medical domain, an efficient CAD system for primary benign i.e., hemangioma (HEM) and primary malignant i.e., hepatocellular carcinoma (HCC) liver lesions based on texture features derived from B-Mode liver ultrasound images of Focal liver lesions has been proposed in the present study. The texture features have been computed from the inside regions of interest (IROIs) i.e., from the regions inside the lesion and one surrounding region of interest (SROI) for each lesion. Texture descriptors are computed from IROIs and SROIs using six feature extraction methods namely, FOS, GLCM, GLRLM, FPS, Gabor and Laws’ features. Three texture feature vectors (TFVs) i.e., TFV1 consists of texture features computed from IROIs, TFV2 consists of texture ratio features (i.e., texture feature value computed from IROI divided by texture feature value computed from corresponding SROI) and TFV3 computed by combining TFV1 and TFV2 (IROIs texture features + texture ratio features) are subjected to classification by SVM and SSVM classifiers. It is observed that the performance of SSVM based CAD system is better than SVM based CAD system with respect to (a) overall classification accuracy (b) individual class accuracy for atypical HEM class and (c) computational efficiency. The promising results obtained from the proposed SSVM based CAD system design indicates its usefulness to assist radiologists for differential diagnosis between primary benign and primary malignant liver lesions.