Automatic clinical image segmentation using pathological modelling, PCA and SVM

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

Volumn 3587 LNAI, Issue , 2005, Pages 314-324

  Li, Shuo ^a   Fevens, Thomas ^a   Krzyzak, Adam ^a   Li, Song ^b  

^a Dept of Electrical and Computer Engineering   (Canada)

^b ANHUI MEDICAL UNIVERSITY   (China)

Author keywords

Dental X rays; Image segmentation; Machine learning; Principal component analysis; Support vector machine

Indexed keywords

BIOMEDICAL ENGINEERING; LEARNING SYSTEMS; PATHOLOGY; PRINCIPAL COMPONENT ANALYSIS; X RAYS;

CLINICAL SEGMENTATION; DENTAL X-RAYS; LEARNING STAGE; SUPPORT VECTOR MACHINE;

IMAGE SEGMENTATION;

EID: 26944488648     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11510888_31     Document Type: Conference Paper

References (20)

1. S. Wang, W. Zhu, and Z.-P, Liang, "Shape deformation: SVM regression and application to medical image segmentation," in ICCV, pp. 209-216, 2001.
2. S. Li, T. Fevens, and A. Krzyżak, "An SVM based framework for autonomous volumetric medical image segmentation using hierarchical and coupled level sets," in Computer Aided Radiology and Surgery (CARS), (Chicago, USA), pp. 207-212, 2004. 10 Li et al.
3. S. Li, T. Fevens, and A. Krzyżak, "Image segmentation adapted for clinical settings by combining pattern classification and level sets," in Medical Image Computing and Computer-Assisted Intervention (MICCAI), (St-Malo, France), pp. 160-167, 2004.
4. L. M. Lorigo, W. E. L. Grimson, O. D. Faugeras, R. Keriven, R. Kikinis, A. Nabavi, and C.-F. Westin, "Codimention-two geodesic active contours for the segmentation of tubular structures," in IEEE Conf- Computer Vision and Pattern Recognition, 2000.
5. S. Osher and J. A. Sethian, "Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations," Journal of Computational Physics, vol. 79, pp. 12-49, 1988.
6. R. Kimmel and A. M. Bruckstein, "Regularized Laplacian zero crossings as optimal edge integrators," International Journal of Computer Vision, vol. 53, pp. 225-243, July 2003.
7. A. Vasilevskiy and K. Siddiqi, "Flux maximizing geometric ow," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 12, pp. 1565-1578, 2002.
8. S. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, energy/bayes/mdl for multiband image segmentation," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19(9), pp. 884-900, 1996.
9. V. Caselles, F. Catte, T. Coll, and F. Dibos, "A geometric model for active contours," Numer. Math., vol. 66, pp. 1-31, 1993.
10. S. Li, T. Fevens, A. Krzyżak, and S. Li, "Level set segmentation for computer aided dental X-ray analysis," in SPIE Conference on Medical Imaging, (San Diego, USA), 2005. Accepted.
11. T. Chan and L. Vese, "Active contour model without edges," IEEE Trans. on Image Processing, vol. 24, pp. 266-277, 2001.
12. L. Vese and T. Chan, "A multiphase level set framework for image segmentation using the mumford and shah model," International Journal of Computer Vision, vol, 50, no. 3, pp. 271-293, 2002.
13. C. Samson, L. Blanc-Fraud, G. Aubert, and J. Zerubia, "A level set model for image classification," International Journal of Computer Vision, vol. 40, no. 3, pp. 187-197, 2000.
14. M. Turk and A. Pentland, "Face recognition using eigenfaces," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, (Hawaii), 1991.
15. M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
16. J. Dong, A. Krzyżak, and C. Y. Suen, "A fast parallel optimization for training support vector," in International Conference on Machine Learning and Data Mining (MLDM) (P. Perner and A. Rosenfeld, eds.), vol. LNAI 2734, (Leipzig, Germany), pp. 96-105, Springer Lecture Notes in Articial Intelligence, July 2003.
17. J. Dong, A. Krzyżak, and C. Y. Suen, "Fast SVM training algorithm with decomposition on very large training sets," IEEE Trans. on Pattern Analysis and Machine Intelligence, 2005. to appear.
18. C.-C. Chang and C.-J. Lin, "Training nu-support vector classifiers: theory and algorithms," Neural Computation, vol. 13, no. 9, pp. 2119-2147, 2001.
19. M. Holtzman-Gazit, D. Goldsher, and R. Kimmel, "Hierarchical segmentation of thin structure in volumetric medical images," in Medical Image Computing and Computer-Assisted Intervention (MICCAI), (Montreal), 2003.
20. A. Tsai, A. Yezzi, and A. S. Willsky, "Curve evolution implementation of the mumford-shah functional for image segmentation, denoising, interpolation, and magnication," IEEE Trans. on Image Processing, vol. 10, pp. 1169-1186, 2001.