Unsupervised segmentation of ultrasonic liver images by multiresolution fractal feature vector

Abstract

The feasibility of selecting fractal feature vector based on multiresolution analysis to segment suspicious abnormal regions of ultrasonic liver images is described in this paper. The proposed feature extraction algorithm is based on the spatial-frequency decomposition and fractal geometry. Segmentation of various liver diseases reveals that the fractal feature vector based on multiresolution analysis is trustworthy. A quantitative characterization based on the proposed unsupervised segmentation algorithm can be utilized to establish an automatic computer-aided diagnostic system. As well, to increase the visual interpretation capability of ultrasonic liver image for junior physicians, off-line learning software is developed to investigate the visual criteria.