结合全局概率密度差异与局部灰度拟合的超声图像分割

由于超声图像具有高噪声、低对比度、边缘模糊不清等特点, 超声图像的分割成为图像处理领域中一个难度较高、亟待解决的问题. 本文提出了一种结合全局概率密度差异与局部灰度拟合的主动轮廓模型对超声图像进行分割的方法. 该方法分别在原始超声图像与预处理图像上利用了图像的全局和局部信息. 在原始图像上, 利用各区域的灰度分布, 并结合超声图像的背景知识对图像的全局信息建模. 为了考虑图像的局部信息, 首先对图像进行预处理, 在预处理图像上, 利用局部灰度拟合模型对图像中的局部信息进行建模. 通过分别在不同图像上对全局和局部信息建模的方式, 本方法将利用Speckle噪声与去除Speckle噪声的分割思想结合在一起. 本文提出的方法分别在模拟和临床超声图像上进行了实验. 实验结果证明, 该方法对图像中的噪声具有较好的适应性, 并对初始条件不敏感, 可以准确地对超声图像进行分割.

Combining Global Probability Density Difference and Local Gray Level Fitting for Ultrasound Image Segmentation

Because of low signal-noise ratio (SNR), low contrast and blurry boundaries, the segmentation of ultrasound image becomes a difficult problem in the digital image processing field. In this paper, a novel active contour model combining global probability density difference and local gray level fitting is proposed for the segmentation of ultrasound image. In the proposed model, global information and local information are extracted in original ultrasound image and pre-processed image, respectively. In the original ultrasound image, by combining the background knowledge the distributions of gray levels of different regions are utilized for modeling the global information. For considering the local information, the ultrasound image is pre-processed, and in the pre-processed image, the local gray level fitting model is utilized for modeling the local information. By modeling the global and the local information in different images, the proposed method combines both approaches that utilize and remove speckle noise. With both simulated and clinical ultrasound images, the experimental results demonstrate that the proposed method is adaptive to the noise and robust to the initial conditions, and that it can segment ultrasound image accurately.