Object Boundary Detection in Ultrasound Images

This paper presents a novel approach to boundary detection of regions-of-interest (ROI) in ultrasound images, more specifically applied to ultrasound breast images. In the proposed method, histogram equalization is used to preprocess the ultrasound images followed by a hybrid filtering stage that consists of a combination of a nonlinear diffusion filter and a linear filter. Subsequently the multifractal dimension is used to analyse the visually distinct areas of the ultrasound image. Finally, using different threshold values, region growing segmentation is used to the partition the image. The partition with the highest Radial Gradient Index (RGI) is selected as the lesion. A total of 200 images have been used in the analysis of the presented results. We compare the performance of our algorithm with two well known methods proposed by Kupinski et al. and Joo et al. We show that the proposed method performs better in solving the boundary detection problem in ultrasound images.