基于快速最小距离树的肝脏图像非刚性配准  

Nonrigid Registration Based on Minimum Spanning Tree in Liver Image

作　　者：陆雪松[1] 刘坤 谢勤岚[1] Lu Xuesong;Liu Kun;Xie Qinlan(College of Biomedical Engineering,South-Central University for Nationalities,Wuhan 430074,China)

机构地区：[1]中南民族大学生物医学工程学院,武汉430074

出　　处：《中南民族大学学报（自然科学版）》2018年第3期48-52,67,共6页Journal of South-Central University for Nationalities：Natural Science Edition

基　　金：国家自然科学基金资助项目(61002046);湖北省自然科学基金资助项目(2016CFB489)

摘　　要：为精确诊疗肝脏疾病,针对CT肝脏图像中呈现出与相邻器官灰度值相似、边界不清晰,仅靠灰度水平法难获得理想的分割结果的问题,采用快速最小距离树度量多维特征,构造了Renyiα-entropy能量函数,成功完成了atlas和待分割图像之间的非刚性配准,实现了肝脏的自动精准分割.该方法的分割精度较传统互信息法明显提高,较传统Prim算法分割速度更快.Segmentation of liver from CT image is difficult to obtain ideal results only by grey level method,due to the similarity of its gray scale with the adjacent organs and the unclear boundaries.To diagnose and treat liver illness accurately,Renyiα-entropy energy function was constructed,which employed minimum spanning tree to measure multidimensional features.Nonrigid registration between the atlas and unsegmented image was thus successfully completed and automatic liver segmentation was realized.The segmentation accuracy of this method is significantly improved compared with the conventional mutual information method,and the speed is faster than traditional Prim′s algorithm.

关 键 词：肝脏分割 非刚性配准 最小距离树 联合Renyiα-entropy 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]