基于目标检测和区域生长的断层图像自动分割
Automatic Segmentation of Tomographic Images Based on Object Detecting and Region Growing Algorithms
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摘要: 为了实现对序列断层图像的自动分割,提出了基于目标检测和区域生长的自动分割方法.基于待分割目标在相邻层上的相关图和相关度的定义,相关图用于表达目标在相邻断层之间的延续关系.采用目标检测算法计算出当前层上已分割图像和相关图中目标的形状参数,包括目标质心和最小外包矩形等,根据相关度为在相邻层上应用区域生长算法提供有效种子点.实验结果表明,该方法能达到序列断层图像自动分割的目的,而且其效率比基于体素的三维区域生长分割方法提高了近50%.Abstract: The authors put forward an automatic segmentation algorithm for tomographic images based on the combination of object detecting and region growing algorithms.The relation graph and correlation degree are firstly defined by the strict rules based on the continuity of the object to be segmented between two sequential images.Then several useful shape parameters of the object in the previous segmented image and relation graph,including the center and the least surrounding boxes,are obtained by object detecting algorithm.Finally effective seed points,which are generated according to the correlation degree,is used to initialize the parameters of region growing algorithm for the latter slice.The experimental results show that the new algorithm can automatically segment serial images,and improve efficiency by a factor of about 50% compared to the algorithm of voxel-based region growing.