基于约束随机游走的肿瘤图像分割方法

doi:10.11896／j.issn.1002-137X.2018.07.042

计算机科学 ›› 2018, Vol. 45 ›› Issue (7): 243-247.doi: 10.11896／j.issn.1002-137X.2018.07.042

所属专题：医学图像

基于约束随机游走的肿瘤图像分割方法

刘庆烽¹,刘哲¹,宋余庆¹,朱彦²

江苏大学计算机科学与通信工程学院江苏镇江212013¹;
江苏大学附属医院江苏镇江212013²

收稿日期:2017-05-25 出版日期:2018-07-30 发布日期:2018-07-30
作者简介:刘庆烽(1994-),男,硕士生,主要研究方向为医学图像处理;刘哲(1982-),女,副教授,主要研究方向为图像处理、图像数据库,E-mail:1000004088@ujs.edu.cn(通信作者);宋余庆(1959-),男,教授,主要研究方向为数据挖掘、知识发现、图像数据库系统等;朱彦(1984-),男,主要研究方向为神经影像诊断。
基金资助:
本文受国家自然科学基金项目(61402204,61572239),江苏大学基金资助项目(14JDG141),镇江市社会发展项目(SH2016029)资助。

Tumor Image Segmentation Method Based on Random Walk with Constraint

LIU Qing-feng¹,LIU Zhe¹,SONG Yu-qing¹,ZHU Yan²

School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China¹;
Affiliated Hospital of Jiangsu University,Zhenjiang,Jiangsu 212013,China²

Received:2017-05-25 Online:2018-07-30 Published:2018-07-30

摘要/Abstract

摘要： 精确的肺部肿瘤区域分割对于放射治疗和手术计划的制定至关重要。针对目前基于单模态图像的肺部肿瘤区域分割的精度较低等问题,综合PET和CT图像的优缺点,提出一种全新的多模态肺部肿瘤图像分割方法。首先,使用区域生长法和数学形态学法对PET图像进行预分割以获取初始轮廓,初始轮廓用于获取PET图像和CT图像上随机游走所需的种子点,同时作为约束加入到CT图像的随机游走过程中;依据CT图像解剖特征较强的特点,利用CT解剖特征改进PET图像上随机游走的权值;最终将PET图像和CT图像上随机游走所获得的相似度矩阵进行加权,在PET图像和CT图像上获得一个相同的分割轮廓。实验表明,相较于其他传统分割算法,所提方法在肺部肿瘤区域分割上具有更高的精确度和更好的稳定性。

关键词: PET-CT, 多模态医学图像, 随机游走, 图像分割

Abstract: Accurate lung tumor segmentation is critical to the development of radiotherapy and surgical procedures.This paper proposed a new multimodal lung tumor image segmentation method by combining the advantages and disadvantages of PET and CT to solve the weakness of single-mode image segmentation,such as the unsatisfied segmentation accuracy.Firstly,the initial contour is obtained by the pre-segmentation of PET image through using region growing and mathematical morphology.The initial contour can be used to automatically obtain the seed points required for random walk of PET and CT images,at the same time,it can be also used as a constraint in the random walk of CT image to solve the shortcoming that the tumor area is not obvious if the CT image has not been enhanced.For the reason that CT provides essential details on anatomic structures,the anatomic structures of CT can be used to improve the weight of random walk on PET images.Finally,the similarity matrices obtained by random walk on PET and CT image are weighted to obtain an identical result on PET and CT images.Clinical PET-CT image segmentation of lung tumorshows that the proposed method has better performance than other traditional image segmentation methods.

Key words: Image segmentation, Multimodal medical image, PET-CT, Random walk

中图分类号:

TP391

刘庆烽, 刘哲, 宋余庆, 朱彦. 基于约束随机游走的肿瘤图像分割方法[J]. 计算机科学, 2018, 45(7): 243-247. https://doi.org/10.11896／j.issn.1002-137X.2018.07.042

LIU Qing-feng, LIU Zhe, SONG Yu-qing, ZHU Yan. Tumor Image Segmentation Method Based on Random Walk with Constraint[J]. Computer Science, 2018, 45(7): 243-247. https://doi.org/10.11896／j.issn.1002-137X.2018.07.042

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基于约束随机游走的肿瘤图像分割方法

Tumor Image Segmentation Method Based on Random Walk with Constraint

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