New ultrasound image-segmentation algorithm based on an early vision model and discrete snake model

Segmentation is a fundamental step in many quantitative analysis tasks for clinical ultrasound images. However, due to the speckle noises and the ill-defined edges of the object of interest, the classic image segmentation techniques are frequently ineffective in segmenting ultrasound images. It is either difficult to identify the actual edges or the derived boundaries are disconnected in the images. In this paper, we present a novel algorithm for segmentation of general ultrasound images, which is composed of two major techniques, namely the early vision model and the discrete snake model. By simulating human early vision, the early vision model can highlight the edges and, at the same time, suppress the speckle noises in an ultrasound image. The discrete snake model carries out energy minimization on the distance map rather than performing snake deformation on the original image as other snake models did. Moreover, instead of searching the next position for a snaxel along its searching path pixel by pixel, the discrete model only consider the local maxima as the searching space. The new segmentation algorithm has been verified on clinical ultrasound images and the derived boundaries of the object of interest are quite consistent with those specified by medical doctors.