LOCAL RELATIVE GLRLM-BASED TEXTURE FEATURE EXTRACTION FOR CLASSIFYING ULTRASOUND MEDICAL IMAGES

This paper presents a new approach of extracting local relative texture feature from ultrasound medical images using the Gray Level Run Length Matrix (GLRLM) sed global feature. To adapt the traditional global approach of GLRLM-based feature extraction method, a three level partitioning of images has been proposed that enables capturing of local features in terms of global image properties. Local relative features are then calculated as the absolute difference of the global features of each lower layer partition sub-block and that of its corresponding upper layer partition block. Performance of the proposed local relative feature extraction method has been verified by applying it in classifying ultrasound medical images of ovarian abnormalities. Besides, significant improvement has been noticed by comparing the proposed method with traditional GLRLM-based feature extraction method in terms of image classification performance.