An Ultrasound Image Segmentation Scheme Based on Edge Confidence and Shape Similarity
基于边缘信赖度和形状相似性的超声图像分割方案

In this paper we propose a new ultrasound image segmentation scheme for segmentation of low SNR ultrasound images, which consisted of anisotropic diffusion function and snake model. An improved snake model based on shape similarity was presented. This model is able to change parameters of snake model adaptively according to shape similarity between the snake curve and the prior shape information. Furthermore, edge confidence was introduced into anisotropic diffusion method in order to improve its denoising performance. Experiments show that the proposed scheme not only resolves the segmentation difficulty resulted from low signal-to-noise rate which was instinctive nature of ultrasound images, but also provides a method for choosing parameters adaptively in snake models. Various experimental results for synthesized and real images show that this scheme is promising.