Texture Analysis of Ultrasound B-Mode Images by Segmentation

B-mode texture characterization by supervised methods of pattern recognition is subject to the following drawbacks: precise localization of the lesion to characterize is often difficult and, even when the lesion is well isolated, its texture can be corrupted by the presence of tumor non specific structures. These structures are not easily discernable and introduce a bias in the statistical measures. The results presented in this paper show that these problems can be circumvented by the use of an unsupervised method of image segmentation. The method enhances the B-mode image and partitions the non specific structures and the lesion texture in different regions which can be characterized independently by statistical methods. The unsupervised approach also facilitates the clinical diagnosis done by visual inspection, by revealing subtle characteristics of the B-mode textures.