M. C. Clark, L. O. Hall, D. B. Goldgof, R. Velthuizen, F. R. Murtagh et al., Automatic tumor segmentation using knowledgebased techniques, IEEE Trans. on Medical Imaging, vol.17, issue.2, pp.238-251, 1998.
DOI : 10.1109/42.700731

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.8043

S. D. Olabarriaga and A. W. Smeulders, Interaction in the segmentation of medical images: A survey, Medical Image Analysis, vol.5, issue.2, pp.127-142, 2001.
DOI : 10.1016/S1361-8415(00)00041-4

M. Lenvine and S. Shaheen, A modular computer vision system for image segmentation, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.3, issue.5, pp.540-557, 1981.

A. Yezzi, S. Kichenassamy, A. Kumar, P. J. Olver, and A. Tannenbaum, A geometric snake model for segmentation of medical imagery, IEEE Transactions on Medical Imaging, vol.16, issue.2, pp.199-209, 1997.
DOI : 10.1109/42.563665

URL : https://hal.archives-ouvertes.fr/hal-00002725

T. Mcinerney and D. Terzopolous, Deformable models in medical image analysis: a survey, Medical Image Analysis, vol.1, issue.2, pp.91-108, 1996.
DOI : 10.1016/S1361-8415(96)80007-7

D. L. Pham and J. L. Prince, An Adaptive Fuzzy Segmentation Algorithm for Three-Dimensional Magnetic Resonance Images, Lecture Notes in Computer Science, vol.1613, pp.140-153, 1999.
DOI : 10.1007/3-540-48714-X_11

L. Clarke, R. Velthuizen, M. Camacho, J. Heine, M. Vaydianathan et al., MRI segmentation: Methods and applications, Magnetic Resonance Imaging, vol.13, issue.3, pp.343-368, 1995.
DOI : 10.1016/0730-725X(94)00124-L

K. I. Kim, K. Jung, S. H. Park, and H. J. Kim, Support vector machines for texture classification, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.24, issue.11, pp.1542-1550, 2002.

B. Schölkopf and A. J. Smola, Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond, 2002.

G. Rätsch, T. Onoda, and K. Müller, Soft margins for AdaBoost, Machine Learning, pp.287-320, 2001.

K. Chan, T. Lee, P. A. Sample, M. Goldbaum, and R. N. Weinreb, Comparison of machine learning and traditional classifiers in glaucoma diagnosis, IEEE Transactions on Biomedical Engineering, vol.49, issue.9, pp.963-974, 2002.
DOI : 10.1109/TBME.2002.802012