A New Splitting Active Contour Framework Based on Chan-Vese Piecewise Smooth Model

根据 Chan-Vese 模型，为图象分割的一个新切开的活跃轮廓方法被介绍。主要想法追随者将在每次重复把一幅图象划分成二部分，它类似于房间切开的过程。然后，模型能在图象检测所有目标或细节。另外，它享受在图象处理任何特定的区域的优点，甚至不顺序的。而分割被限制到兴趣(ROI ) 而非整个图象的区域，这直接导致计算效率的改进。由于操作的地区性的限制，而且，我们的模型超过存在多相的 Chan-Vese 模型以到初始化的敏感。我们的模型的原则详细被描述，并且方法在水平集合框架下面被实现。合成、医药的图象的实验被执行，并且比较级结果到 Chan-Vese 模型和多相的 Chan-Vese 模型也被显示出。

On the basis of the Chan-Vese model, a new splitting active contour method for image segmentation is presented. The main idea following is to divide an image into two parts at every iteration, which is similar to the procedure of cell splitting. Then, the model is able to detect all the objects or details in the image. In addition, it enjoys the merit of processing any specific region in the image, even the inconsecutive one. This directly leads to the improvement of computing efficiency whereas segmentation is limited to region of interest （ROI） rather than the whole image. Furthermore, due to the regional constraint of operation, our model outperforms the existing multiphase Chan-Vese model in terms of sensitivity to the initialization. The principle of our model is described in detail, and the method is implemented under the level set framework. Experiments on both synthetic and medical images are carried out, and the comparative results to Chan-Vese model and multiphase Chan-Vese model are also shown.