Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI

IEEE Transactions on Medical Imaging

Volumn 15, Issue 2, 1996, Pages 154-169

  Johnston, B ^a   Atkins, M S ^a   Mackiewich, B ^a   Anderson, M ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BRAIN; DISEASES; IMAGE SEGMENTATION; MATHEMATICAL MORPHOLOGY; NUMERICAL ANALYSIS; RADIOLOGY; RANDOM PROCESSES; SIGNAL FILTERING AND PREDICTION; STATISTICAL METHODS; THREE DIMENSIONAL; TISSUE;

HOMOMORPHIC FILTERING; INTENSITY CORRECTED MULTISPECTRAL MRI; MULTIPLE SCLEROSIS LESIONS; PARTIAL VOLUME ANALYSIS; STOCHASTIC RELAXATION METHOD;

MAGNETIC RESONANCE IMAGING;

EID: 0030126711     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/42.491417     Document Type: Article

References (50)

1. [ l ] R.W. Aibright Jr. and E. K. Fram, Microcomputer-based teclinique for 3-D reconstruction and volume measurement of computed tomographic images-Part i: Phantom studies, Investigat. Radial., vol. 23, no. 12, pp. 881-885, Dec. 1988
2. P. Lundin and U. Pedersen. Volume of pituitary macroadenomas: Assessment by MRI, J. Comput. Assist. Tomogr., vol. 16, no. 4, pp. 519-528, July/Aug. 1992
3. P. Maeder, A. Wirsen, M Bajc, W. Schalen, H. Sjoholm, H. Skeidsvoll, S. Cronoqvist, and D. H. Ingvar, Volumes of chromis traumatic frontal brain lesions measured by MR imaging and CBF tomography, Acta Radiologien, vol. 32, pp. 271-278, 1991
4. D. Williams, P. Bland, L.Liu, L. Farjo, I. R. Francis, and C. R. Meyer, Liver-tumour boundary detection: Human observer versus computer edge detection, Investigat. Radial., 24, no. 10, pp. 768-775, Oct. 1989
5. J. K. Udupa, S. Samarasekera, and W. A. Barrett. Boundary detection via dynamic programming, in Proc. SPIE: Visualization in Binmed. Comput. 1992, Chapel Hill, NJ, 1992, vol. 1808, pp. 33-39
6. D. T. Long, M. A. King, and B. C. Penney, 2-D versus 3-D edge detection as a basis for volume quantitation in SPECT. Inform. Processing Med. Imag., pp. 457-471, 1991
7. M. Joliot and B. M. Mazoyer, Three-dimensional segmentation and interpolation of magnetic resonance brain images, JEEE Trans. Med. Imag., 12, no. 2. pp. 269-277, June 1993
8. H. Suzuki and J. Toriwaki. Automatic segmentation of head MRI images by knowledge guided thresholding, Computerized Med. Imag. Graphics, 15, no. 4, pp. 233-240, Jul-Aug 1991.
9. F. Lachmann and C. Barillot, Brain tissue classification from MRI data by means of texture analysis, in Proc. SPIE: Med. Imag. VI: Image Processing, Chapel Hill, NJ, 1992, vol. 1652, pp. 72-83
10. R. E. GUT, P. D. Mozley, S. M. Resnick, D. Shtasel, M. Kohn, R. Zimmerman, G. Herman, S. Atlas, R. Grossman, R. Erwin, and R. C. Gur, Magnetic resonance imaging in schizoplirenia: I. volumetric analysis of brain and cerebrospinal fluid, Archives Gen. Psych., vol. 48, pp. 407-112, May 1991
11. M. I. Kohn, N. K. Tanna, G. T. Herman, S. M. Resniek, P. D. Mosley, R. E. Gur, A. Alavi, R. A. Zimrnerman, and R. C. Gur, Analysis of brain and cerebrospinal fluid volumes with (MR) imaging-Part i: Methods, reliability and validation, Radial., 178, no. 1, pp. 115-122, Jan. 1991
12. J. R. Mitchell, S. J. Karlik, D. H. Lee, and A. Fenster, Automated detection and quantification of multiple sclerosis lesions in MR volumes of the brain, in Proc. SPIE: Medical Imaging VI: Image Processing, Chapel Hill, NJ, 1992, vol. 1652, pp. 99-106
13. T. Sandor, D. Metcalf, and Y. Kirn, Segmentation of brain CT images using the concept of region growing, Int. J. Biomed. CompuL. vol. 29, pp. 143-147, 1991
14. R. R. Stringham, W. A. Barrett, and D. C. Taylor , Probabilistic segmentation using edge detection and region growing, in Proc. SPIE: Visualization Biomed. Comput. 1992, Chapel Hill, NJ, 1992, vol. 1808, pp. 40-51
15. K. H. Hohne and W. H. Hanson, Interactive 3-D segmentation of MRI and CT volumes using morphological operations, J. Comput. Assist. Tomogr., vol. 16, no. 2, pp. 285-294, 1992
16. N. J. Mankivich, L. I. Rudin, G. Koepfier, J. M. Morel, and S. Osher, Application of a new pyramidal segmentation algorithm to medical images, in Proc. SPIE: Medical Imaging VI: Image Processing, Chapel Hill, NJ, 1992, vol. 1652, pp. 23-30
17. C. Mathieu, I. E. Magnin, and C. Baldy-Porcher, Optimal stochastic pyramid: Segmentation of MRI data, in Proc. SPIE: Medical Imaging VI: Image Processing, Chapel Hill, NJ, 1992, vol. 1652, pp. 14-22
18. A. Rosenfeld, Computer vision: A source of models for biological visual processes, IEEE Trans. Biomed. Eng., 36, no. 1, pp. 93-96, 1989
19. L. Arata, A. Dhawan, J. Broderick, and M. Gaskill, Three dimensional model-guided segmentation and analysis of medical images, in Proc. SPIE: Medical Imaging VI: Image Processing, Chapel Hill, NJ, 1992, vol. 1652, pp. 253-259
20. M. Kamber, D. L. Collins, R. Shinghal, G. S. Francis, and A. C. Evans, Model-based 3-D segmentation of multiple sclerosis lesions in dualecho MRI data, in Proc. SPIE: Visualization Biomed. Comput. 1992, Chapel Hill, NJ, 1992, vol. 1808, pp. 590-600
21. H. S. Choi, D. R. Haynor, and Y. Kirn, Partial volume tissue classification of multichannel magnetic resonance images-a mixel model, IEEE Trans. Med. Imag., 10, no. 3, pp. 395-407, Sept. 1991
22. K. R. Smith and L. A. Kendrick Bayesian computer vision methods for improved tumor localization and delineation, in Proc. IEEE Med.. Imag. Conf., Santa Fe, NM, 1991, pp. 2140-2144
23. B. Johnston, M. S. Atdns, and K. S. Booth, Partial volume segmentation in 3-D of lesions and tissues in magnetic resonance images, in Proc. SPIE-Medical Imaging 1994, Bellingham, WA, 1994. SPIE-The Int. Soc. Opt. Eng., 2167, pp. 28-39
24. L. J. Rosner and S. Ross, Multiple Sclerosis. New York: Simon and Schuster, 1992
25. G. J. Ettinger et al. Automatic registration for multiple sclerosis change detection, in Proc. IEEE Workshop Biomed. Image Anal., Los Alamitos, CA, June 1994, IEEE Comput. Soc. Press, pp. 297-306.
26. F. Pannizzo, M. J. B. Stallmeyer, J. Friedman, R. J. Jennis, J. Zabriskie, C. Pland, R. Zimmerman, J. P. Whalen, and P. T. Cahill, Quantitative MRI studies for assessment of multiple sclerosis, Magn. Resonance. Med., vol. 24, pp. 90-99, 1992
27. Alex P. Zijdenbos, Benoit M. Dawant, Richard A. Margolin, and Andrew C. Palmer, Morphornetric analysis of white matter lesions in MR images: Method and validation, IEEE Trans. Med. Imag., vol. 13, no. 4, pp. 716-724, Dec. 1994
28. W. M. Wells III, W. E. L. Grimson, R. Kikinis, and F. A. Jolesz, /- vito intensity correction and segmentation of magnetic resonance image data, in AAAl 1994 Spring Symp. Ser., AAAI, Mar. 1994
29. M. Kamber, R. Shinghal, D. L. Collins, G. S. Francis, and A. C. Evans, Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images, IEEE Trans. Med. Imag., 1995
30. D. W. Patey and D. K. B. Li, Interferon beta-lb is effective in relapsing-remitting multiple sclerosis-Part ii: MRI analysis results of a multicenter, randomized, double-blind, placebo-controlled trial, Neural., 43:662-667, Apr. 1993.
31. B. Johnston, 3-D multispectral stochastic image segmentation, Master's thesis, The Univ. of British Columbia, Computer Science Dept., Vancouver, British Columbia, Canada, Jan. 1994
32. R. Acharya and Y. Ma. Segmentation algorithms for cranial magnetic resonance images, in Proc. SPIE: Medical Imaging VI: Image Processing, Chapel Hill, NJ, 1992, vol. 1652, pp. 50-61
33. M. E. Brummer, R. M. Mersereau, R. L. Eisner, and R. R. J. Lewine, Automatic detection of brain contours in MRI data sets, IEEE Trans. Med. Imag., vol. 12, no. 2, pp. 153-166, June 1993
34. H. E. Clinc, W. E. Lorcnscn, R. Kikinis, and F. Jolcsz, Three dimensional segmentation of MR images of the head using probability and connectivity, J. Cotnput. Assist. Tofnogr., vol. 14, no. 6, pp. 1037-1045, 1990
35. B. Mackiewich, Intracranial boundary detection and radio frequency correction in magnetic resonance images, Master's thesis, Simon Fraser Univ., Computer Science Dept., Burnaby, British Columbia, Aug. 1995
36. L. Axel, J. Costantini, and J. Listcrud, Intensity correction in surfacecoil MR imaging, Amer. J. Roentgenol.. vol. 148, pp. 418-20, Feb. 1987
37. J. Haselgrove and M. Prammer, An algorithm for compensation of surface-coil images for sensitivity of the surface coil, Magn. Resonance Imag., vol. 4, no. 6, pp. 469-472, 1986
38. W. W. Brey and P. A. Narayana, Correction for intensity falloff in surface coil magnetic resonance imaging, Med. Phys., vol. 15, no. 2, pp. 241-245, Mar./Apr. 1988
39. n. T. Glennon, L. P. Clarke, R. P. Velthuizen, and F. A. Jolesz, MR image nonuniformity correction techniques, in Proc. 13th Annu. Meet. IEEE Eng, Med. Biol. Soc., IEEE EMBS, Orlando, Florida, Oct./Nov. 1991 pp. 89-90
40. K. O. Lim and A. Phefferbaum, Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter, J. Comput. Assist. Tomogr., 13, no. 4, pp. 588-593, July/Aug. 1989.
41. P. A. Narayana, W. W. Brey, M. V. Kulkarni, and C. L. Sievenpiper, Compensation for surface coil sensitivity variation in magnetic resonance imaging, Magn. Resonance Imag., vol. 6, no. 3, pp. 271-274, 1988
42. B. M. Dawant, A. P. Zijdenbos, and R. A. Marolin, Correction of intensity variations in MR images for computer-aided tissue classification, IEEE Trans. Med. Imag., vol. 12, no. 4, pp. 770-781, Dec. 1993
43. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Reading, MA: Addison-Wesley, 1992
44. R. B. Lufkin, T. Sharpless, B. Flannigan, and W. Hanafee, Dynamicrange compression in surface-coil MRI, Amer. J. Roentgenol., vol. 147, pp. 379-382, Aug. 1986
45. J. Besag, On the statistical analysis of dirty pictures, J. Royal Statistical Soc., vol. 48, no. 3, pp. 259-302, 1986
46. B. Johnston, M. S. Atkins, and K. S. Booth, Three-dimensional partial volume brain tissue segmentation of multispectral magnetic resonance images using stochastic relaxation, in Non-Linear Image Processing VI, vol. 2180, E. R. Dougherty, Ed. Bellingham, WA: SPIE-The International Soc. for Optical Engineering, pp. 268-279, 1994
47. W. Tsai, Moment-preserving thresholding: A new approach, Comput. Vision. Graphics Image Processing, vol. 29, pp. 377-393, 1985
48. S. U. Lee and S. Y. Chung, A comparative performance study of several global thresholding techniques for segmentation, Comput. Vision, Graphics Image Processing, vol. 52, pp. 171-190, 1990
49. P. K. Sahoo, S. Soltani, and K. C. Wong, A survey of thresholding techniques, Comput. Vision, Graphics, Irnage Processing, vol. 41, pp. 233-360, 1988
50. M. W. Vannier, T. K. Pilgrim, C. M. Speidel, L. R. Neumann, D. L. Rickman, and L. D. Schertz, Validation of magnetic resonance imaging (MRI) multispectral tissue classification, Comput. Med. Imag. Graphics, vol. 15, no. 4, pp. 217-223, 1991.