窄带主动轮廓模型及在医学和纹理图像局部分割中的应用

Narrow Band Active Contour Model for Local Segmentation of Medical and Texture Images

提出一种新的基于二值水平集的窄带主动轮廓模型用于局部分割.通过对二值水平集进行形态学膨胀和腐蚀操作,提出一种稳定灵活可控的窄带控制方案,该方案可使得曲线进化精度从一个像素宽度灵活变化到任意大小.考虑到局部分割一般要求初始轮廓置于待分割目标内部并不断膨胀进化直至目标边缘,本文提出用形态学闭运算作为新的曲线平滑方案.与传统的高斯平滑和曲率平滑方案相比,形态学闭运算不仅能够更好地促进曲线的膨胀进化,而且有利于保持水平集函数的二值性.此外,本文提出的方法是一种通用的自然框架,可以根据不同的需求设计不同的速度函数.为了证明所提出的局部分割框架的有效性和鲁棒性,本文以医学图像和纹理图像为例分别设计了两个速度函数:一个是融合了磁共振脑图像的非严格对称信息的速度函数用于大脑皮质下结构的局部分割;另一个是融合了局部熵和局部梯度算子的速度函数用于纹理图像的局部分割.在合成图像、医学图像和纹理图像上的实验证明了本文方法在局部分割中的有效性和鲁棒性.

A new narrow band active contour model based on binary level set function （LSF） for local segmentation is proposed in this paper. By taking morphological dilation and erosion operations on the binary LSF, a stable and flexible narrow band is built, and the curve evolution precision （CEP） can change from one to infinity flexibly. Considering that the contour will be initialized inside the target object and inflated afterwards in local segmentation, morphological closing operation is utilized to smooth the binary LSF. Comparing with Gaussian filtering and curvature term, morphological closing operation will not only facilitate curve inflation more effectively, but also maintain the binary property of LSF. Moreover, the proposed model is a local segmentation framework, so different speed functions can be designed for different kinds of images. In order to demonstrate the effectiveness and robustness of the framework, we choose medical and texture images as examples. Meanwhile, two speed functions are designed respectively. One is for subcortical brain structures segmentation in MR brain images fused with the non-strict symmetric information, and the other is for texture segmentation combined local entropy and local gradient operators. Experiments on some synthetic, medical and texture images demonstrate the effectiveness and robustness of the proposed method in object segmentation.