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

Abstract

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.