As for the inhomogenous images, it is difficult and ineffective to segment Regions Of Interest (ROI). In order to solve these problems, this paper proposes an image segmentation algorithm based on the active contour model. Different from the ones in traditional level set techniques, which only use single information, a new energy function is defined by combining object edge information and regional statistical information. Utilization of edge information is in favor of the contours evolving into the object boundaries quickly and accurately. Regional statistical information consists of both local and global statistical information inside and outside the evolving contours. On the one hand, utilization of local region information facilitates the method to deal with intensity inhomogeneity. On the other hand, using global region information can avoid the evolved contour trapping into the local minima. In addition, in the evolution process of the contour, a Gaussian filter is adopted to quickly regularize the level set function, which avoids an expensive computational re-initialization or regularization. Experimental results using synthetic and real images show that the proposed approach can not only effectively segment objects with the weak boundaries in inhomogenons images, but also accurately segment the complex structure objects with multi-gray levels. At the same time, the method is robust to noise and the initial contour.%针对非均匀灰度图像分割困难及分割效率低下的问题,该文提出了一种基于活动轮廓模型的高效图像分割算法.不同于传统水平集方法中仅用单一信息定义的能量泛函,该算法结合图像的边缘信息和区域统计信息定义了一个新的能量泛函.边缘信息的利用便于演化轮廓线快速精确地定位至物体边缘;区域统计信息由局部统计信息和全局统计信息构成,一方面,局部统计信息的利用能够有效处理图像的灰度分布不均匀现象,另一方面,全局统计信息的利用避免了轮廓线陷入局部极小值.最后,在轮廓线演化过程中,通过高斯卷积核实现快速规则化,避免了传统模型计算代价高昂的重新初始化或规贝则化.合成图像和真实图像的实验结果表明,该文算法不仅能够快速有效分割灰度分布不均匀的弱边缘物体,而且对于多灰阶复杂结构物体也能够精确分割;同时,该算法对噪声和初始轮廓线具有较好的鲁棒性.
展开▼
