CASIA OpenIR  > 毕业生  > 博士学位论文
计算机辅助肝外科手术规划中医学图像分割算法研究
Alternative TitleStudy of Medical Image Segmentation Algorithms Based on Computer-aided Liver Surgery Planning
张星
Subtype工学博士
Thesis Advisor田捷
2012-05-29
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword计算机辅助手术规划 腹部ct图像 肝脏分割 肝肿瘤分割 肝脏血管分割与分析 Computer-aided Surgery Planning Abdominal Ct Images Liver Segmentation Liver Tumor Segmentation Liver Vessel Segmentation And Analysis
Abstract肝脏是人体最大的实质性脏器,在新陈代谢中有重要作用。全世界每年肝癌死亡人数达到一百万,肝癌的死亡率位居我国癌症死亡率第二位,是严重影响人民健康水平的疾病。传统的肝外科手术规划和评估等工作多停留在针对病历文档和二维医学影像的简单层面,对于复杂的肝外科手术具有盲目性和不可靠性。计算机辅助肝外科手术规划利用影像学技术、图像处理及计算机图形学技术,辅助外科医师进行疾病诊断监控、肿瘤切除、活体肝脏移植,可以最大程度减少术后并发症,提高手术的客观性和成功率。计算机辅助肝外科手术规划一般使用多层螺旋CT数据进行分析处理和三维可视化,可提供全方位的脏器、肿瘤、肝内管道系统的解剖结构及其三维空间关系。 医学图像分割是计算机辅助肝外科手术规划的关键问题,涉及CT图像中肝脏、肝肿瘤、脉管系统的分割以及肝段划分。CT图像中肝脏相关组织分割是后续三维可视化、定量分析和手术规划的前提。其难点包括:待分割目标与周围的组织存在弱边缘、形状及纹理变化较大,肝脏血管拓扑结构复杂、存在大量分支,图像存在噪声和伪影。如何进行快速精确的肝脏组织分割这一挑战性问题成为最近的研究热点。 本文针对CT数据中肝脏、肝肿瘤以及肝脏血管的快速精确分割提出了相应的算法,并基于医学影像平台MITK和3DMed实现相关算法,主要工作内容包括: 1) 提出了一种基于统计形状模型及最优表面检测的肝脏分割方法。该算法采用广义Hough变换对形状模型进行初始定位,并在后处理中采用基于图的最优表面搜索算法克服统计形状模型方法灵活性差的缺点。该算法在MICCAI 2007肝脏分割竞赛的数据集上进行测试,实验结果证明该算法充分利用了肝脏的形状先验知识并能灵活地拟合肝脏边缘,对临床肝脏数据能实现快速精确地分割。 2) 提出了一种基于分水岭变换及支持向量机分类的交互式肝肿瘤分割算法。该算法对原始数据采用分水岭变换进行过分割。利用用户交互得到的数据进行训练,在过分割后的集水盆区域提取区域特征向量,采用基于区域的支持向量机分类方法对肿瘤和正常肝组织进行分类。该算法在MICCAI 2008肝肿瘤分割竞赛的数据集上进行测试,实验结果证明,该算法与肝肿瘤分割使用的基于体素的分类方法相比,在不损失精度的前提下显著提高了运行效率。 3) 提出了一种基于中心线上空心球模板的血管树分支点检测算法及实现了一种血管分割及分析框架。该框架包括基于多尺度Hessian矩阵滤波的血管增强、血管分割、基于通量的中心线提取及基于中心线上空心球模板的血管树分支点检测及图表示。我们在临床数据、MICCAI 2007肝脏分割竞赛及MICCAI 2008肝肿瘤分割竞赛的数据集上对算法进行测试,实验结果表明,我们的方法能有效地对肝脏血管进行分割和分析。
Other AbstractLiver is the largest solid organ of the body and plays a major role in metabolism. About one million people worldwide die from liver cancer each year and the disease ranks second in cancer mortality in China. The disease seriously jeopardizes health and life of people. Traditional liver surgery planning and evaluation only use case history and 2D medical image of patient, which is blind and unreliable when employed in complex liver surgery. Computer-aided liver surgery planning depends on medical imaging, image processing and computer graphics technology to help the surgeon with disease diagnostic and surveillance, oncologic resections, living-related liver transplants. It can reduce postoperative complications and improve the objectivity and survival rate of surgery. In order to explore anatomy of hepatic organ, tumor, vasculature and their spacial relationship, computer-aided liver surgery planning generally utilizes Multi-slice Spiral Computed Tomography (MSCT) data for analysis, processing and 3D visualization. The key problem of computer-aided liver surgery planning is medical image segmentation, which includes liver segmentation, liver tumor segmentation, hepatic vasculature segmentation and liver segments. Hepatic tissue segmentation in CT scans is the prerequisite for visualization, quantitative analysis and surgery planning. It has some difficult tasks including: (1) weak boundary between object and some adjacent tissues; (2) highly varying shape and appearance of object; (3) complex topology and high number of bifurcations of liver vessel; (4) image noise and artifacts. Efficient and accurate hepatic tissue segmentation becomes a challenging task that has attracted research attention recently. In this dissertation, we presented algorithms for segmentation of liver, liver tumor and liver vessel in CT scans and implemented the algorithms based on the medical imaging platform (MITK&3DMed) developed by our group. The main contents include: 1) We proposed a liver segmentation method using a statistical shape model (SSM) integrated with an optimal surface detection strategy. The approach firstly employs 3D generalized Hough transform to localize the SSM and finally employs a graph-based optimal surface detection algorithm to overcome the disadvantage of lacking flexibility of SSM. The method was tested on MICCAI 2007 liver segmentation challenge datasets. Experimental results show that our method can explore liver shape prior abundantly and adap...
shelfnumXWLW1751
Other Identifier200918014628071
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/6439
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
张星. 计算机辅助肝外科手术规划中医学图像分割算法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2012.
Files in This Item:
File Name/Size DocType Version Access License
CASIA_20091801462807(16014KB) 暂不开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[张星]'s Articles
Baidu academic
Similar articles in Baidu academic
[张星]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[张星]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.