基于局部熵的区域活动轮廓图像分割模型

Regional Active Contour Image Segmentation Model Based on Local Entropy

为解决区域活动轮廓模型不能有效分割灰度不均图像的问题,提出了局部熵约束的区域活动轮廓模型应用于图像分割。首先基于局部熵信息将图像划分为两个特征区域,然后利用局部熵特征信息构造二值拟合能量,并与区域可放缩拟合(Region⁃scalable fitting,RSF)模型相结合,最后得到水平集演化方程。该模型考虑了图像灰度分布的聚集特征和局部区域统计信息,能有效处理灰度不均匀、弱边缘等图像分割问题,且对轮廓初始位置更具鲁棒性,医学图像实验结果验证了模型的有效性。

To solve the problem that the regional active contour model cannot effectively segment weak targets,a regional active contour model with local entropy constraints is proposed for image segmentation.Firstly,the image is divided into two feature regions based on local entropy information.Then a local entropy binary fitting energy is constructed by using local entropy feature information,and finally a level set evolution equation is obtained,which is combined with a region-scalable fitting(RSF)model.The model considers the clustering characteristics of the gray distribution and the statistical information of the local area of the image,and it is effective in handing intensity inhomogeneity,weak edge segmentation,and flexible contour initialization.Medical image experiment results verify the effectiveness of the proposed model.