基于ITK的MR脑组织图像分割方法的研究
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摘要
目的
利用ITK对颅脑MR图像进行半自动及自动分割,并对分割所得的脑组织结构(白质、灰质、脑室)图像进行观察、研究,分析优缺点,为颅脑MR图像脑组织结构的三维显示及手术导航做准备。
材料与方法
利用ITK编程读取DICOM格式的MR脑图像,半自动分割采用连续阈值区域生长算法、自动分割采用K-Means聚类算法对41幅图像进行分割,得到白质、灰质、脑室等脑组织结构。
结果
连续阈值区域生长算法及K-Means聚类算法都很好的分割出了各部分脑组织结构。连续阈值区域生长算法综合分割速度慢,受不同层上不同脑组织像素间连通性的影响,将MR脑图像上的各脑组织结构分次单幅显示出来了,较K-Means对不包含脑室的图像进行4分类时的分割精度高、细节多。K-Means聚类算法分割的综合速度比区域生长分割方法分割的快,受初始聚类中心、聚类准则函数、相似度度量方法的影响,可以一次性将白质、灰质及脑室等结构在一幅图像上分割后显示出来,对包含脑室结构的图像进行五分类时的分割效果明显优于连续阈值区域生长的分割效果,而且这样一次性将各脑组织结构在一幅图像上分割出来的快速、高效的分割思想及模式是今后图像分割的方向。
结论
不同分割方法的优缺点、侧重点不一样,加上医学图像的各异性、复杂性、多模态性,以及图像分割的目的及要求的多样性、具体性、特殊性,使得具体问题需具体研究、分析。连续阈值区域生长算法及K-Means聚类算法的分割各有特点,都很好的分割出了各部分脑组织结构,可以据需要将它们应用于颅脑MR图像脑组织结构的三维显示及手术导航中。
Objective
To segment brain tissue from MR Images semi-automaticly and automaticly respectively with ITK. Then to observe and study the segmented brain tissue(whitematter, graymatter, ventricle)images so as to analyze the advantages and disadvantages of those segmented brain tissue images that will be made preparation for the 3D visualization and surgery navigation of MR brain tissue.
Material and Methods
To segment 41 sheets of MR brain image with DICOM format in the ITK and receive corresponding brain tissue(whitematter, graymatter, ventricle) images.Semi-automatic segmentation adopts connected threshold region growing algorithm, while automatic segmentation adopts K-Means clustering algorithm.
Results
Because of the diversity of connectivity among pixels of different brain tissue in the different slices, connected threshold region growing algorithm segments specific MR Images with 3 sheets of images including leftwhitematter, rightwhitematter and graymatter or 3 sheets of images including whitematter, graymatter and ventricle or 4 sheets of images including leftventricle, rightventricle, whitematter and graymatter. Meanwhile, under the influence of initial means, clustering criterion functions and semblance metric methods, K-Means clustering algorithm segments whitematter, graymatter and ventricle on one sheet of image. Both algorithms obtain high quality of corresponding brain tissue image. On the aspect of overall segmentation speed, region growing algorithm is slower than K-Means clustering algorithm: region growing algorithm receives one sheet of one brain tissue image after one segmentation, while K-Means clustering algorithm receives one sheet of all brain tissue image after one segmentation. Meanwhile, on the aspect of segmentation precision and details, region growing algorithm is better than K-Means 4 clustering while K-Means 5 clustering is better than region growing algorithm. The fast effective segmentation mode of K-Means clustering algorithm which receives one sheet of all brain tissue image after one segmentation is the orientation of image segmentation in the future.
Conclusion
Because of the different strong points, advantages and disadvantages of different segmentation algorithms, otherness, complexity and multimodality of medical images and diversity, materiality and particularity of the aims and requests of medical image segmentation, concrete issue should be analyzed concretely. Region growing algorithm and K-Means clustering algorithm segment MR brain images and receive different high quality brain tissue images that can be applied to the 3D visualization and surgery navigation of MR brain tissue.
利用ITK对颅脑MR图像进行半自动及自动分割,并对分割所得的脑组织结构(白质、灰质、脑室)图像进行观察、研究,分析优缺点,为颅脑MR图像脑组织结构的三维显示及手术导航做准备。
材料与方法
利用ITK编程读取DICOM格式的MR脑图像,半自动分割采用连续阈值区域生长算法、自动分割采用K-Means聚类算法对41幅图像进行分割,得到白质、灰质、脑室等脑组织结构。
结果
连续阈值区域生长算法及K-Means聚类算法都很好的分割出了各部分脑组织结构。连续阈值区域生长算法综合分割速度慢,受不同层上不同脑组织像素间连通性的影响,将MR脑图像上的各脑组织结构分次单幅显示出来了,较K-Means对不包含脑室的图像进行4分类时的分割精度高、细节多。K-Means聚类算法分割的综合速度比区域生长分割方法分割的快,受初始聚类中心、聚类准则函数、相似度度量方法的影响,可以一次性将白质、灰质及脑室等结构在一幅图像上分割后显示出来,对包含脑室结构的图像进行五分类时的分割效果明显优于连续阈值区域生长的分割效果,而且这样一次性将各脑组织结构在一幅图像上分割出来的快速、高效的分割思想及模式是今后图像分割的方向。
结论
不同分割方法的优缺点、侧重点不一样,加上医学图像的各异性、复杂性、多模态性,以及图像分割的目的及要求的多样性、具体性、特殊性,使得具体问题需具体研究、分析。连续阈值区域生长算法及K-Means聚类算法的分割各有特点,都很好的分割出了各部分脑组织结构,可以据需要将它们应用于颅脑MR图像脑组织结构的三维显示及手术导航中。
Objective
To segment brain tissue from MR Images semi-automaticly and automaticly respectively with ITK. Then to observe and study the segmented brain tissue(whitematter, graymatter, ventricle)images so as to analyze the advantages and disadvantages of those segmented brain tissue images that will be made preparation for the 3D visualization and surgery navigation of MR brain tissue.
Material and Methods
To segment 41 sheets of MR brain image with DICOM format in the ITK and receive corresponding brain tissue(whitematter, graymatter, ventricle) images.Semi-automatic segmentation adopts connected threshold region growing algorithm, while automatic segmentation adopts K-Means clustering algorithm.
Results
Because of the diversity of connectivity among pixels of different brain tissue in the different slices, connected threshold region growing algorithm segments specific MR Images with 3 sheets of images including leftwhitematter, rightwhitematter and graymatter or 3 sheets of images including whitematter, graymatter and ventricle or 4 sheets of images including leftventricle, rightventricle, whitematter and graymatter. Meanwhile, under the influence of initial means, clustering criterion functions and semblance metric methods, K-Means clustering algorithm segments whitematter, graymatter and ventricle on one sheet of image. Both algorithms obtain high quality of corresponding brain tissue image. On the aspect of overall segmentation speed, region growing algorithm is slower than K-Means clustering algorithm: region growing algorithm receives one sheet of one brain tissue image after one segmentation, while K-Means clustering algorithm receives one sheet of all brain tissue image after one segmentation. Meanwhile, on the aspect of segmentation precision and details, region growing algorithm is better than K-Means 4 clustering while K-Means 5 clustering is better than region growing algorithm. The fast effective segmentation mode of K-Means clustering algorithm which receives one sheet of all brain tissue image after one segmentation is the orientation of image segmentation in the future.
Conclusion
Because of the different strong points, advantages and disadvantages of different segmentation algorithms, otherness, complexity and multimodality of medical images and diversity, materiality and particularity of the aims and requests of medical image segmentation, concrete issue should be analyzed concretely. Region growing algorithm and K-Means clustering algorithm segment MR brain images and receive different high quality brain tissue images that can be applied to the 3D visualization and surgery navigation of MR brain tissue.
引文
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[3] Zhu C,Jiang T.Mufti-context fuzzy clustering for separation of brain tissues in MR images[J].NeuroImage,2003,18(3):685-696.
[4] Liew AWC,Yan H.An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation[J].IEEE Trans.on Medical Imaging,2003,22(9): 1063- 1075.
[5] Ahmed MN,Yamany SM.A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data[J].IEEE Trans. on Medical Imaging,2002,21(3):193-199.
[6] Pham DL,Prince JL.Adaptive fuzzy segmentation of magenetic resonace images[J].IEEE Trans. on Medical Imaging,1999,18(9):737-752.
[7] Mohamed NA,Sameh MY,Nevin M.A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data[J].IEEE Trans. On Medical Imaging,2002,21(3):193-199.
[8] Nie SD,Chen Y,Gu SD,et al.The study of fast fuzzy clustering segmentation algorithm of head MRI[J].Chinese Journal of Biomedical Engineering, 2001,20(2):104-109.
[9]李彬.基于模糊随机模型的磁共振脑部图像分割算法研究.第一军医大学博士学位论文,2007.
[10]李娜.脑磁共振图像分割方法研究.中南大学硕士学位论文,2007.
[11]冯衍秋.基于Gibbs随机场理论的脑部MR图像分割新算法研究.第一军医大学硕士学位论文,2003.
[12]武倩.磁共振脑图像中肿瘤化组织的特征提取和分割.清华大学硕士学位论文,2005.
[13]王顺凤,张建伟.基于Gibbs场与模糊C均值聚类的脑MR图像分割[J].计算机应用,2008,28(7):1750-1752.
[14] Li SZ.Markov Random Field Modeling in Image Analysis[M]. Springer -Verlag,2001.
[15] Deng HW.Clausi DA.Unsupervised image segmentation using a simple MRF model with a new implementation scheme[J].Pattern Recognition, 2004,37(12):2323-2335.
[16] Zhang Y,Brady M, Smith S.Segmentation of brain images through a hidden Markov random field model and the expectation-maximization algorithm[J]. IEEE Trans. on Medical Imaging,2001,20(1):45-57.
[17] J.K.Udupa.Fuzzy connectedness and image segmentation[J].Proceedings of the IEEE,2003,91(10):1649-1669.
[18] J.K.Udupa,S.Samarasekera.Fuzzy connectedness and object definition: theory,algorithms,and applications in image segmentation[J].Graphical model and Image processing,1996,58(3):246-261.
[19]宋利伟,宋朝昀,庄天戈.基于分水岭算法的磁共振脑图像自动分割[J].上海交通大学学报,2003,37(11):1754-1771.
[20] Cheng KS.The application of competitive Hopfield neural network to medical image segmentation[J].IEEE Trans. On Medical Imaging, 1996 (15):560-567.
[21]李响,罗述谦.多谱MR脑图象的组织分类[J].中国生物医学工程学报, 1999,18(3):289-294.
[22] Liang Z,Macfall J R,Harrington D P.Parameter estimation and tissue segmentation from multispectral MR images[J].IEEE Trans. On Medical Imaging,1994,13(3):441-449.
[23] Zhukov L,Museth K,Breen D,et al.Level set modeling and segmentation of DT-MRI brain data[J].Journal of Electronic Imaging,2003,12(1):125-133.
[24]周震,马斌荣.结合脑图谱和水平集的MR图像分割的研究[J].中国医学物理学杂志,2006,23(5):329-332,344.
[25] Suri,J.S,Leaking prevention in fast level sets using fuzzy models:An application in MR brain[J].In Proc.Int.Conf.Inform.Technol.Biomedicine, 2000:220-226.
[26] Mitiche A.Optical flow 3D segmentation and interpretation: a variational method with active curve evolution and level sets[J].IEEE Trans. Pattern Anal Mach Intell,2006,28(11):1818-1829.
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