A NEW GEOMETRIC ACTIVE CONTOUR FOR MEDICAL IMAGE SEGMENTATION
一种新的用于医学图像分割的几何活动轮廊模型

Generally, the segmentation of a medical image is difficult, because the medical image is often corrupted by norrupted by noise, and the anatomical shape in the medical image is complicated. In this paper presents a new geometric active contour scheme for medical image segmentation. First, we regularize the attraction force field in the geometric active contour model to extend the capture range of the object boundaries, and improve the ability of convergence to the concavities. Then, using a multi-scale scheme improve the boundary detection accuracy. In addition, combining the regularization and the multi-scale method, the proposed scheme can effectively suppress and eliminate the noise and the spurious edges in the medical images. Furthermore, the topology of the deforming curve can naturally change without and special topolygy handing procedures added to the scheme. This permits synchronously extracting several anatomical structures. The experiments on some medical images obtained from different medical imaging methods demonstrate that the proposed approach is competent for medical image segmentation.