基于局部特征的自适应快速图像分割模型

Adaptive Fast Image Segmentation Model Based on Local Feature

基于区域的活动轮廓模型如Chan-Vese(CV)模型等以其能较好的处理图像的模糊边界和复杂拓扑结构而广泛运用于图像分割中.然而基于灰度分布均匀假设,该模型对于含灰度不一致性的目标分割结果较差.此外,纹理是周期性重复出现的细节,依靠灰度信息无法正确检测.针对这些问题,提出一种基于局部特征的自适应快速图像分割模型.一方面,利用两种区域项检测卡通部分和纹理部分的特征信息,在自适应的局部块中提取局部统计信息以克服卡通部分的灰度不一致性;另一方面,利用自适应的局部块中的纹理特征来计算背景和目标区域的Kullback-Leibler(KL)距离以检测图像的纹理部分.进一步,基于分裂Bregman方法对该模型进行快速求解.分别对医学和纹理图像进行了实验,准确性和时效性都有显著提高.

Region-based active contour model such as Chan-Vese model is able to handle the blurry boundary and complex topological structures in images segmentation. However, based on the intensity-homogeneous distribution, the effect on segmentation in the images with intensity inhomogeneity is not fine. Textures are fine scale-details, usually with some periodicity nature, and they cannot be detected by intensity information. Aiming at these problems, an adaptive fast image segmentation model based on local features is proposed. On the one hand, two kinds of region data terms are designed for detecting cartoon and texture parts respectively. The local statistic information is extracted in the adaptive patch to solve the over-segmentation induced by the intensity inhomogeneities. On the other hand, the texture feature information calculated in the adaptive patch acts to compute the Kullback-Leibler distance for detecting the texture part. Our model is solved by the split Bregman method for efficiency. Experiments are carried on both medical and texture images to compare our approach with some competitors, demonstrating the precision and efficiency of our the model.