A probabilistic framework for the detection and tracking in time of multiple sclerosis lesions

2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano

Volumn 1, Issue , 2004, Pages 440-443

  Shahar, Allon ^a   Greenspan, Hayit ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BRAIN; GAUSSIAN NOISE (ELECTRONIC); IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; MATHEMATICAL MODELS; NEUROLOGY; PROBABILITY; STATISTICAL METHODS;

GAUSSIAN MIXTURE MODELS (GMM); IMAGING TOOLS; MULTIPLE SCLEROSIS (MS) LESIONS; REGIONS OF INTEREST (ROI);

MEDICAL IMAGING;

EID: 17144393726     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

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2. Gerig, G., et al., "Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data", Medical Image Analysis, vol. 4(1), pp. 31-42, Mar. 2000.
3. Greenspan, H., J. Goldberger, and A. Mayer, "A probabilistic framework for spatio-temporal video representation and indexing", In Proceedings to the 7th European Conference on Computer Vision (Computer Vision - ECCV 2002), vol. 2353, Springer-Verlag, Berlin, Germany, pp. 461-75, 2002.
4. Greenspan, H., A. Mayer, and A. Shahar, "A Probabilistic Framework for the Spatio-Temporal Segmentation of Multiple Sclerosis Lesions in MR Images of the Brain", In Proceedings of SPIE International Symposium on Medical Imaging, San Diego, USA, pp. 1551-1559, Feb. 2003.
5. Kikinis, R., et al., "Quantitive follow-up of patients with multiple sclerosis using MRI: technical aspects", Journal of Magnetic Resonance Imaging, vol. 9(4), pp. 519-530, 1999.
6. Rey, D., et al., "A Spatio-temporal Model-based Statistical Approach to Detect Evolving Multiple Sclerosis Lesions", In IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01), Kauia, Hawaii, USA, pp. 105-112, 2001.
7. Van Leemput, K., et al., "Automated segmentation of multiple sclerosis lesions by model outlier detection", IEEE Trans. on Medical Imaging, vol. 20, pp. 677-688, Aug. 2001.