基于边缘信赖度和形状相似性的超声图像分割方案

为了对低信噪比的超声图像进行有效分割,提出了一种新的超声图像分割方案,该方案由各向异性扩散方程和蛇模型组成。首先通过对蛇模型算法进行改进,并利用预先知道的形状信息,提出了一种基于形状相似性的参数自调整蛇模型;同时还对各向异性扩散方程进行了修正,提出了基于边缘信赖度的改进算法,以提高各向异性扩散方程的去噪能力。实验结果表明,该方法不但缓解了由于超声图像信噪比过低而影响分割的问题,同时实现了蛇模型的参数自适应设置,可见是一种有效的图像分割算法。

An Ultrasound Image Segmentation Scheme Based on Edge Confidence and Shape Similarity

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.