关键词: 图像分割, 主动轮廓模型, 全局统计建模信息, 混合灰度分布, 能量泛函

Abstract:

Image segmentation is an indispensable key step in digital image processing. In order to solve the problem that the traditional active contour model is inaccurate for the non-homogeneous image segmentation and the segmentation efficiency is low, this paper proposes an active contour image segmentation algorithm combined with statistical modeling of distribution metrics. The energy driving force of the proposed algorithm takes into account the global statistical modeling information of the image and other mixed gray distribution information, so that the segmentation curve can be more accurately evolved to the target edge. Firstly, the distribution metric energy driving force is defined as the variance of the ratio distance defined by the probability density function inside and outside the contour. The energy driving force is based on the statistical modeling of the image global information, which can more accurately describe the energy variation inside and outside the contour curve. Secondly, the mixed gray-scale distribution energy driving force is represented by the L2 norm of the image fitting value center of the image gray value and the fusion mean and median. Finally, the distribution metric energy driving force and the mixed gradation distribution energy driving force are combined to form a novel energy function, and the minimum value of the energy function is obtained by using the level set method and the gradient descent method to obtain the final image segmentation result. Compared with the image segmentation experiment results of four algorithms, such as traditional CV（Chan Vese） model and LBF（Local Binary Fitting） model, the proposed model has great advantages in the subjective evaluation effect, sensitivity to the initial contour, running time and number of iterations.

Key words: image segmentation, active contour model, global statistical modeling information, mixed gray distribution information, energy function