Probabilistic spatial-temporal segmentation of multiple sclerosis lesions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3117, Issue , 2004, Pages 269-280

  Shahar, Allon ^a   Greenspan, Hayit ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER VISION; IMAGE PROCESSING; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; MAXIMUM LIKELIHOOD; MAXIMUM PRINCIPLE; MEDICAL IMAGING;

CONTEXT-BASED CLASSIFICATION; EXPECTATION-MAXIMIZATION ALGORITHMS; GAUSSIAN MIXTURE MODEL; MAXIMUM LIKELIHOOD PRINCIPLE; MULTIPLE SCLEROSIS; MULTIPLE SCLEROSIS LESIONS; QUANTITATIVE RESULT; UNSUPERVISED CLUSTERING;

IMAGE ANALYSIS;

EID: 35048890224     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-27816-0_23     Document Type: Article

References (14)

1. Greenspan, H., Goldberger, J., Mayer, A.: Probabilistic Space-Time Modeling via Piecewise GMM. IEEE Trans. Pattern Analysis and Machine Intelligence 26:3 (Mar. 2004) 384-396
2. Grossman, R.I., McGowan, J.C.: Perspectives on multiple sclerosis. American Journal of Neuroradiology 19 (Aug. 1998) 1251-1265
3. Noseworthy, J.H., Lucchinetti, C., Rodriguez, M., Weinshenker, B.G.: Multiple Sclerosis. The New England Journal of Medicine 343:13 (2000) 938-952
4. Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. on Medical Imaging 20 (Aug. 2001) 677-688
5. Warfield, S., Dengler, J., Zaers, J., Guttmann, C.R.G., Wells, W.M., Ettinger, G.J., Killer, J., Kikinis, R.: Automatic Identification of Grey Matter Structures from MRI to Improve the Segmentation of White Matter Lesions. Journal of Image Guided Surgery 1:6 (1995) 326-338
6. Wei, X., Warfield, S.K., Zou, K.H., Wu, Y., Li, X., Guimond, A., Mugler III, J.P., Benson, R.R., Wolfson, L., Weiner, H.L., Guttmann, C.R.G.: Quantitative Analysis of MRI Signal Abnormalities of Brain White Matter with High Reproducibility and Accuracy. Journal of Magnetic Resonance Imaging 15 (2002) 203-209
7. Kikinis, R., C.R.G. Guttmann, D. Metcalf, W.M. Wells, G.J. Ettinger, H.L. Weiner, Jolesz, F.A.: Quantitative follow-up of patients with multiple sclerosis using MRI: technical aspects. Journal of Magnetic Resonance Imaging 9:4 (1999) 519-530
8. Gerig, G., Welti, D., Guttmann, C.R.G., Colchester, A.C.F., Székely, G.: Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data. Medical Image Analysis 4:1 (Mar. 2000) 31-42
9. Rey, D., Stoeckel, J., Malandain, G., Ayache, N.: 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 (Dec. 2001) 105-112
10. Rey, D., Subsol, G., Delingette, H., Ayache, N.: Automatic Detection and Segmentation of Evolving Processes in 3(d) Medical Images: Application to Multiple Sclerosis. Medical Image Analysis 6:4 (Dec. 2002) 163-179
11. Dempster, A., Laird, N., Rubin, D.: Maximum Likelihood from incomplete data via the EM algorithm. J. of Royal Statistical Society 39:1 (1977) 1-38
12. Greenspan, H., Mayer, A., Shahar, A.: 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 (2003) 1551-1559
13. Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Series in Telecommunication, John Wiley and Sons, New York (1991)
14. Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Quering. IEEE Trans. on Pattern Analysis and Machine Intelligence 24:8 (Aug. 2002) 1026-1038