摘要:
基于 CV 模型, 提出了新的活动轮廓线分裂模型. 其基本思想相似于细胞分裂, 即在每次迭代中一分为二. 该模型能检测出图像中所有的目标和细节; 能处理处理图像中特定区域, 甚至是不连通区域; 并且, 由于图像分割可以被限制在感兴趣区域而不是整个区域, 从而提升了计算性能; 此外, 由于其具有区域约束, 因此不同于 CV 模型的对初始化敏感. 论文不仅详细分析了模型的基本原理, 而且用水平集方法实现了, 并成功应用于合成图像和真实图像, 其分割结果与 CV 模型以及多项 CV 模型进行了比较.
Abstract:
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.
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