TY - CONF
T1 - Multi-tissue Classification of Diffusion-Weighted Brain Images in Multiple System Atrophy Using Expectation Maximization Algorithm Initialized by Hierarchical Clustering
T2 - 13th International Conference on Biomedical Engineering, ICBME 2008
AU - Lu, Chia-Feng
AU - Wang, Po-Shan
AU - Soong, Bing-Wen
AU - Chou, Yen-Chun
AU - Li, Hsiao-Chien
AU - Wu, Yu-Te
AU - al, AMTI; BES Technology; DELSYS; VICON; INSTRON; et
N1 - 會議代碼: 101867
Export Date: 31 March 2016
通訊地址: Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, No.155, Sec.2, Linong Street, Taipei, Taiwan
參考文獻: Liu, T., Li, H., Wong, K., Tarokh, A., Guo, L., Wonga, T.C., Brain tissue segmentation based on DWI data (2007) NeuroImage, 38, pp. 114-123; Wishart, D., An algorithm for hierarchical classifications (1969) Biometrics, 25, pp. 165-170; Wu, Y.T., Chou, Y.C., Guo, W.Y., Yeh, T.C., Hsieh, J.C., Classification of spatiotemporal hemodynamics from brain perfusion MR images using expectation-Maximization estimation with finite mixture of multivariate Gaussian distributions (2007) Magnetic Resonance in Medicine, 57, pp. 181-191; Schwarz, G., Estimating the dimension of a model (1978) Ann. Statist., 6, pp. 461-464; Otsu, N., A threshold selection method from gray-level histogram (1979) IEEE Transactions on Systems, Man, and Cybernetics; Jiang, H., Kim, J., Pearlson, G.D., Mori, S., DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking (2006) Computer Methods and Programs in Biomedicine, 81, pp. 106-116
PY - 2009
Y1 - 2009
N2 - Multiple system atrophy (MSA) is a well-known neurodegenerative disorders that present parkinsonism syndrome and autonomic dysfunction. Patients with MSA who have the combination of parkinsonism and cerebellar ataxia are referred to as MSA-C. Brain diffusion-weighted imaging (DWI) offers the potential for objective criteria in the diagnosis of MSA. We aim to develop an automatic method to segment out the abnormal whole brain area in MSA-C patients based on the 13-direction DWI raw data. The whole brain DWI raw data of fifteen normal subjects and nine MSA-C patients were analyzed. In this study, we proposed a novel method to perform tissue segmentation directly based on the directional information of the DWI images, rather than using the parametric images, such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC) as in the previous literatures. Specifically, a hierarchical clustering (HC) technique was first applied on the down-sampled data to initialize the model parameters for each tissue cluster followed by automatic segmentation using the expectation maximization (EM) algorithm. Our results demonstrate that the HC-EM is effective in multi-tissue classification, namely, the cerebrospinal fluid, gray matter, and several areas of white matters, on the DWI raw data. The segmented patterns and the corresponding intensities of thirteen directions of the cerebellum in MSA-C patients showed the decrease of the anisotropy, which were evidently different from the results in normal subjects.
AB - Multiple system atrophy (MSA) is a well-known neurodegenerative disorders that present parkinsonism syndrome and autonomic dysfunction. Patients with MSA who have the combination of parkinsonism and cerebellar ataxia are referred to as MSA-C. Brain diffusion-weighted imaging (DWI) offers the potential for objective criteria in the diagnosis of MSA. We aim to develop an automatic method to segment out the abnormal whole brain area in MSA-C patients based on the 13-direction DWI raw data. The whole brain DWI raw data of fifteen normal subjects and nine MSA-C patients were analyzed. In this study, we proposed a novel method to perform tissue segmentation directly based on the directional information of the DWI images, rather than using the parametric images, such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC) as in the previous literatures. Specifically, a hierarchical clustering (HC) technique was first applied on the down-sampled data to initialize the model parameters for each tissue cluster followed by automatic segmentation using the expectation maximization (EM) algorithm. Our results demonstrate that the HC-EM is effective in multi-tissue classification, namely, the cerebrospinal fluid, gray matter, and several areas of white matters, on the DWI raw data. The segmented patterns and the corresponding intensities of thirteen directions of the cerebellum in MSA-C patients showed the decrease of the anisotropy, which were evidently different from the results in normal subjects.
KW - diffusion-weighted imaging
KW - expectation maximization algorithm
KW - hierarchical clustering
KW - Multiple system atrophy
KW - Apparent diffusion coefficient
KW - Automatic segmentations
KW - Diffusion weighted imaging
KW - Directional information
KW - Expectation-maximization algorithms
KW - Hier-archical clustering
KW - Multiple system atrophies
KW - Neurodegenerative disorders
KW - Anisotropy
KW - Biomedical engineering
KW - Brain
KW - Brain mapping
KW - Cerebrospinal fluid
KW - Clustering algorithms
KW - Diffusion
KW - Maximum principle
KW - Neurodegenerative diseases
KW - Tissue
KW - Image segmentation
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84891914161&partnerID=40&md5=e82d38776f34b974fbc10bb7433a1848
UR - https://www.scopus.com/results/citedbyresults.uri?sort=plf-f&cite=2-s2.0-84891914161&src=s&imp=t&sid=79c36b2c23fa07a7effac516cebf74ea&sot=cite&sdt=a&sl=0&origin=recordpage&editSaveSearch=&txGid=569b0695be6b6583c73b09fdc6a02a6a
U2 - 10.1007/978-3-540-92841-6_177
DO - 10.1007/978-3-540-92841-6_177
M3 - Other
SP - 722
EP - 725
ER -