基于图像序列上下文关联的肝脏器官分割方法  

Segmentation Method for Liver Organ Based on Image Sequence Context

作　　者：张美云[1] 房斌[1] 王翊[1] 钟南昌[1] 

机构地区：[1]重庆大学计算机学院,重庆400044

出　　处：《生物医学工程学杂志》2015年第5期1125-1130,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金重点资助项目(61190122);国家科技支撑计划资助项目(2012BAI06B01)

摘　　要：针对现有二维分割方法人工干预较多及存在分割缺陷、三维分割方法对突变异常肝脏分割错误等问题,本文提出一种基于图像序列上下文关联的肝脏器官半自动分割方法。利用肝脏器官组织图像序列上下文的相似性先验知识,结合区域生长和水平集模型,并以少量人工干预辅助应对肝脏突变情况来进行肝脏的半自动分割。实验结果表明,本文方法分割肝脏精度高,适应能力强,对变异性较大的肝脏分割效果较好,可较好地满足临床应用需求。In view of the problems of more artificial interventions and segmentation defects in existing two-dimension- al segmentation methods and abnormal liver segmentation errors in three-dimensional segmentation methods, this pa- per presents a semi-automatic liver organ segmentation method based on the image sequence context. The method takes advantage of the existing similarity between the image sequence contexts of the prior knowledge of liver organs, and combines region growing and level set method to carry out semi-automatic segmentation of livers, along with the aid of a small amount of manual intervention to deal with liver mutation situations. The experiment results showed that the liver segmentation algorithm presented in this paper had a high precision, and a good segmentation effect on livers which have greater variability, and can meet clinical application demands quite well.

关 键 词：上下文关联 图像序列 肝脏分割 水平集 区域生长 

分 类 号：R333.4[医药卫生—人体生理学] TP391.41[医药卫生—基础医学]