Segmentation of diffusion-weighted brain images using expectation maximization algorithm initialized by hierarchical clustering

Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"

Volumn , Issue , 2008, Pages 5502-5505

  Lu, Chia Feng ^a   Wang, Po Shan ^b   Chou, Yen Chun ^a   Li, Hsiao Chien ^a   Soong, Bing Wen ^a   Wu, Yu Te ^a  

^a NATIONAL YANG MING UNIVERSITY   (Taiwan)

^b TAIPEI MUNICIPAL GAN DAU HOSPITAL   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; MAXIMUM PRINCIPLE; SURFACE DIFFUSION; TISSUE;

APPARENT DIFFUSION COEFFICIENTS; AUTOMATIC SEGMENTATIONS; BRAIN IMAGES; BRAIN TISSUES; DIFFUSION-WEIGHTED IMAGES; DIRECTIONAL INFORMATIONS; EXPECTATION-MAXIMIZATION ALGORITHMS; FRACTIONAL ANISOTROPIES; HIERARCHICAL CLUSTERING; MODEL PARAMETERS; NEURAL FIBERS; NOVEL METHODS; PARAMETRIC IMAGES; RAW IMAGE DATUM; SAMPLED DATUM; TISSUE CLASSIFICATIONS; TISSUE SEGMENTATIONS;

CLUSTER ANALYSIS;

AGED; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; CLUSTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION WEIGHTED IMAGING; FEMALE; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; MALE; METHODOLOGY; MIDDLE AGED; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING; VERY ELDERLY;

AGED; AGED, 80 AND OVER; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; CLUSTER ANALYSIS; DIFFUSION MAGNETIC RESONANCE IMAGING; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; LIKELIHOOD FUNCTIONS; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 61849103465     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. J.L. Tanabe, D. Amend, N. Schuff, V. DiSclafani, F. Ezekiel, D. Norman, G. Fein, and M.W. Weiner, Tissue segmentation of the brain in Alzheimer disease, Am J Neuroradiol, 18, 1997, 115-123.
2. S.J. Colcombe, K.I. Erickson, N. Raz, A.G. Webb, N.J. Cohen, E. McAuley, and A.F. Kramer, Aerobic fitness reduces brain tissue loss in Aging Humans, Journal of Gerontology: Medical Sciences, 58A, 2003, 176-180.
3. P.S. Huppi, S. Warfield, R. Kikinis, P.D. Barnes, G.P. Zientara, F.A. Jolesz, M.K. Tsuji, and J.J. Volpe, Quantitative magnetic resonance imaging of brain development in premature and mature newborns, Ann Neurol, 43, 1998, 224-235.
4. W.M. Wells, R. Kikinis, WEL Grimson, F. Jolesz, Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging, 15, 1996, 429-442.
5. T. Liu, H. Li, K. Wong, A. Tarokh, L. Guo, and T.C. Wonga, Brain tissue segmentation based on DWI data, NeuroImage, 38, 2007, 114-123.
6. D. Wishart, An algorithm for hierarchical classifications, Biometrics, 25, 1969, 165-170.
7. Y.T. Wu, Y.C. Chou, W.Y. Guo, T.C. Yeh, J.C. Hsieh, Classification of spatiotemporal hemodynamics from brain perfusion MR images using expectation-Maximization estimation with finite mixture of multivariate Gaussian distributions, Magnetic Resonance in Medicine, 57, 2007, 181-191.
8. G. Schwarz, Estimating the dimension of a model, Ann. Statist., 6, 1978, 461-464.
9. N. Otsu, A threshold selection method from gray-level histogram, IEEE Transactions on Systems, Man, and Cybernetics, 1979.
10. S. Mori, P.B. Barker, Diffusion magnetic resonance imaging: its principle and applications, Anat Rec (New Anat), 257, 1999, 102-109.