A statistically based surface evolution method for medical image segmentation: Presentation and validation

Lecture Notes in Computer Science

Volumn 2879, Issue PART 2, 2003, Pages 711-720

  Pichon, Eric ^a   Tannenbaum, Allen ^a   Kikinis, Ron ^b  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BRAIN; COMPUTATIONAL COMPLEXITY; COMPUTER VISION; FUNCTIONS; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; PARAMETER ESTIMATION; STATISTICAL METHODS; SURFACES; MEDICAL COMPUTING; MEDICAL IMAGE PROCESSING; MEDICAL IMAGING;

MEDICAL IMAGE SEGMENTATION; QUANTIFICATION; THERAPY PLANNING; 3D MEDICAL IMAGE; BRAIN STRUCTURE; GLOBAL ENERGY; NON-PARAMETRIC ESTIMATIONS; ROBUSTNESS TO NOISE; SEMI-AUTOMATICS; SOURCE CODES; SURFACE EVOLUTION;

MEDICAL IMAGING; IMAGE SEGMENTATION;

EID: 0344823916     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-39903-2_87     Document Type: Conference Paper

References (20)

1. Zijdenbos A., Dawant B., and Margolin R. Morphometric analysis of white matter lesions in MR images: Method and validation. IEEE TMI, 13(4):716-724, 1994.
2. Huttenlocher D., Klanderman G., and Rucklidge W. Comparing images using the Hausdorff distance. PAMI, 15(9):850-863, 1993.
3. Sethian J. Level Set Methods and Fast Marching Methods, Cambridge University Press, 1999.
4. Siddiqi K., Lauziere Y., Tannenbaum A., and Zucker S. Area and length minimizing flows for shape segmentation. IEEE TMI, 7:433-443, 1998.
5. Strasters K. and Gerbrands J. Three-dimensional segmentation using a split, merge and group approach. Pattern Recognition Letters, 12:307-325, 1991.
6. Simon L. Lectures on geometric measure theory. In Proceedings of the Centre for Mathematical Analysis, Australian National University, Canberra, 1983.
7. Kass M., Witkin A., and Terzopoulos D. Snakes: Active contour models. Int. J. Computer Vision, 1:321-332, 1988.
8. Paragios N. and Deriche R. Geodesic active regions: A new paradigm to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation, 13:249-268, 2002.
9. Adams R. and Bischof L. Seeded region growing. PAMI, 16(6):641-647, 1994.
10. Duda R., Hart P., and Stork D. Pattern Classification. Wiley-Interscience, 2001.
11. Gonzalez R. and Woods R. Digital Image Processing. Prentice Hall, 2001.
12. Justice R., Stokely E., Strobel J., Ideker R., and Smith W. Medical image segmentation using 3-D seeded region growing. Proc. SPIE Symposium on Medical Imaging Volume, 3034:900-910, 1997.
13. Kwan R., Evans A., and Pike G. MRI simulation-based evaluation of image-processing and classification methods. IEEE TMI, 18(11):1085-1097, 1999.
14. Pohle R. and Toennies K. Segmentation of medical images using adaptive region growing. In Proc. SPIE Medical Imaging.
15. Zhu S. and Yuille A. Region competition: Unifying snakes, region growing, and bayes/MDL for multiband image segmentation. PAMI, 18(9):884-900, 1996.
16. Mclnerney T. and Terzopoulos D. Deformable models in medical image analysis: A survey. Medical Image Analysis, 1(2):91-108, 1996.
17. Caselles V., Kimmel R., and Sapiro G. Geodesic active contours. In Proc. ICCV, pages 694-699, 1995.
18. Yasnoff W., Miu J., and Bacus J. Error measures for scene segmentation. Pattern Recognition, 9:217-231, 1977.
19. World Wide Web. http://www.bic.mni.mcgill.ca/brainweb/.
20. Zhang Y. A survey on evaluation methods for image segmentation. Pattern Recognition, 29(8):1335-1346, 1996.