Optimal filter design for multiple sclerosis lesions segmentation from regions of interest in brain MRI

Sixth IEEE International Symposium on Signal Processing and Information Technology, ISSPIT

Volumn , Issue , 2006, Pages 1-5

  Ghazel, Mohsen ^a   Traboulsee, Anthony ^a   Ward, Rabab K ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Multiple sclerosis; Segmentation; Texture filter design

Indexed keywords

DATA REDUCTION; FEATURE EXTRACTION; IMAGE CLASSIFICATION; MAGNETIC RESONANCE IMAGING; PROBLEM SOLVING; SYSTEMS ANALYSIS;

MULTIPLE SCLEROSIS; TEXTURE FEATURES; TEXTURE FILTER DESIGN;

IMAGE SEGMENTATION;

EID: 44449135603     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISSPIT.2006.270759     Document Type: Conference Paper

References (16)

1. R. M. Haralick, K. Shanmugam, J. Dinstein, "Textural features for image classification," IEEE Trans. Syst. Man. Cybern, vol. 6, pp. 610-621, 1973.
2. Gasperini C, Horsfield MA, Thorpe JW, Kidd D, Barker GJ, Tofts PS, MacmManus DG, Thompson AJ, MacDonald WI, Miller EH, "MRI texture in normal appearing white matter of MS patients: relationship to other quantitative MRI and clinical parameters", J. Magn. Reson Imag. 1994; 11:33-42.
3. Goldberg-Zimring, D., et al., "Automated detection and chracterization of multiple sclerosis lesions in brain MR images," Magnetic Resonance Imaging, vol. 16(3), pp. 311-318, 1998.
4. R. I. Grossman and J.C McGowan, "Perspectives on Multiple Sclerosis," AJNR, 19:1251-1265, August 1998.
5. Laws K. T., "Rapid texture identification", Image processing for missible guidance", SPIE, vol. 238, pp 376-380, 1980.
6. Lechtenberg, R., Multiple Sclerosis Fact Book, Second Edition, Philadelphia, 1995.
7. J. M. Mathias, P. S. Tofts, N. A. Losseff, "Texture Analysis of Spinal Cord Pathology in Multiple Sclerosis", Magnhetic Resonance in Medicine, vol. 42, pp. 929-935, 1999.
8. Paty, D. W., and D. K. B. Li, Interferon Beta-lb is Effective in Relapsing- Remitting Multiple Sclerosis. II. MRI Analysis Results of a Multicenter, Randomized, Double-Blind, Placebo-controlled Trial, Neurology, vol. 43, pp. 662667, Apr. 1993.
9. Poser, C. M., et al., New Diagnostic Criteria for Multiple Sclerosis: Guidelines for Research Protocols, Annals of Neurology, vol. 13(3), pp. 227-231, Mar. 1983.
10. Randen T. and Husy J. H., Texture segmentation using filters with optimized energy separation, IEEE Trans. Image Processing, vol. 8, pp. 571582, Apr. 1999.
11. Randen T. and Husy J. H., Filtering for texture classification: A comparative study, IEEE Trans. Pattern Anal. Machine Intell., vol. 21, pp. 291310, Apr. 1999.
12. Thompson, A. J., C. Polman and R. Hohlfeld (editors), Multiple Sclerosis: Clinical Challenges and Controversies, Martin Dunitz, London, 1997.
13. Udupa, J. K., et al., "Multiple Sclerosis Lesion Quantification Using Fuzzy-Connectedness Principles ", IEEE Trans. on Medical Imaging, vol. 16(5), pp. 598-609, Oct. 1997.
14. 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.
15. Warfield, S., et al., "Automatic Identification of Grey Matter Structures from MRI to Improve the Segmentation of White Matter Lesions", J Image Guided Surgery, vol. 1(6), pp. 326-338, 1995.
16. O. Yu, Y. Mauss, G. Zollner, I.J. Namer, and J. Chambron, "Distinct Patterns of Active and Non-active Plaque using Texture Analysis on Brain MRI Images in Multiple Sclerosis Patients: Preliminary Results", Magnetic Resonance Imaging, Vol. 17, No. 9, pp. 1261-1267, 1999.