Automatic clinical image segmentation using pathological modeling, PCA and SVM

Engineering Applications of Artificial Intelligence

Volumn 19, Issue 4, 2006, Pages 403-410

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

^a Civil and Environmental Engineering   (Canada)

^b ANHUI MEDICAL UNIVERSITY   (China)

Author keywords

Dental X ray; Image segmentation; Pathological modeling

Indexed keywords

COMPUTER AIDED DIAGNOSIS; COMPUTERIZED TOMOGRAPHY; MATHEMATICAL MODELS; MEDICAL IMAGING; PRINCIPAL COMPONENT ANALYSIS; X RAY ANALYSIS;

DENTAL X-RAY; PATHOLOGICAL MODELING; SUPPORT VECTOR MACHINE (SVM) CLASSIFIERS;

IMAGE SEGMENTATION;

EID: 33748252478     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2006.01.011     Document Type: Article

References (27)

1. Bergtholdt M., Cremers D., and Schnörr C. Variational segmentation with shape priors. In: Paragios N., Chen Y., and Faugeras O. (Eds). Mathematical Models in Computer Vision: The Handbook (2005), Springer, Berlin
2. Caselles V., Catte F., Coll T., and Dibos F. A geometric model for active contours. Numerical Mathematics 66 (1993) 1-31
3. Caselles V., Kimmel R., and Sapiro G. Geodesic active contours. International Journal of Computer Vision 22 (1997) 61-79
4. Chan T., and Vese L. Active contour without edges. IEEE Transactions on Image Processing 24 (2001) 266-277
5. Chang C.-C., and Lin C.-J. Training nu-support vector classifiers: theory and algorithms. Neural Computation 13 9 (2001) 2119-2147
6. Dong, J., Krzyżak, A., Suen, C.Y., 2003. A fast parallel optimization for training support vector. In: Perner, P., Rosenfeld, A. (Eds.), International Conference on Machine Learning and Data Mining(MLDM). Lecture Notes in Artificial Intelligence, vol. 2734. Springer, Leipzig, Germany, pp. 96-105.
7. Dong J., Krzyżak A., and Suen C.Y. Fast SVM training algorithm with decomposition on very large training sets. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 4 (2005) 603-618
8. Holtzman-Gazit, M., Goldsher, D., Kimmel, R., 2003. Hierarchical segmentation of thin structure in volumetric medical images. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Montreal.
9. Kimmel R., and Bruckstein A.M. Regularized Laplacian zero crossings as optimal edge integrators. International Journal of Computer Vision 53 3 (July 2003) 225-243
10. Li, S., Fevens, T., Krzyżak, A., 2004a. 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.
11. Li, S., Fevens, T., Krzyzak, A., 2004b. A svm-based framework for autonomous volumetric medical image segmentation using hierarchical and coupled level sets. In: CARS, International Congress Series, vol. 1268. Elsevier, Chicago, USA, pp. 207-212.
12. Li, S., Fevens, T., Krzyzak, A., Li, S., 2005a. Automatic clinical image segmentation using pathological modelling, PCA and SVM. In: Perner, P., Imiya, A. (Eds.), Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Artificial Intelligence, vol. 3587. Springer, Leipzig, Germany, pp. 314-324.
13. Li, S., Fevens, T., Krzyżak, A., Li, S., 2005b. An automatic variational segmentation for dental X-ray analysis in clinical environment. Computerized Medical Imaging and Graphics, in press.
14. Li, S., Fevens, T., Krzyżak, A., Li, S., 2005c. Level set segmentation for computer aided dental X-ray analysis. In: SPIE Conference on Medical Imaging, vol. 5747, San Diego, USA, pp. 580-589.
15. Lorigo, L.M., Grimson, W.E.L., Faugeras, O.D., Keriven, R., Kikinis, R., Nabavi, A., Westin, C.-F., 2000. Codimension-two geodesic active contours for the segmentation of tubular structures. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1444-1451.
16. Osher S., and Sethian J.A. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 79 (1988) 12-49
17. Perner P. An architecture for a cbr image segmentation system. Journal on Engineering Application in Artificial Intelligence 12 6 (1999) 749-759
18. Samson C., Blanc-Féraud L., Aubert G., and Zerubia J. A level set model for image classification. International Journal of Computer Vision 40 3 (2000) 187-197
19. Tsai A., Yezzi A., and Willsky A.S. Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Transactions on Image Processing 10 (2001) 1169-1186
20. Turk M., and Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience 3 1 (1991) 71-86
21. Turk, M., Pentland, A., 1991b. Face recognition using eigenfaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Hawaii.
22. Vasilevskiy A., and Siddiqi K. Flux maximizing geometric flow. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 12 (2002) 1565-1578
23. Vese L., and Chan T. A multiphase level set framework for image segmentation using the Mumford and Shah model. International Journal of Computer Vision 50 3 (2002) 271-293
24. Wang, S., Zhu, W., Liang, Z.-P., 2001. Shape deformation: SVM regression and application to medical image segmentation. In: ICCV, pp. 209-216.
25. Yong X., Feng D., Rongchun Z., and Petrou M. Learning-based algorithm selection for image segmentation. Pattern Recognition Letters 26 8 (2005) 1059-1068
26. Zhao H.-K., Chan T.F., Merriman B., and Osher S. A variational level set approach to multiphase motion. Journal of Computational Physics 127 1 (1996) 179-195
27. Zhu S., and Yuille A. Region competition: unifying snakes, region growing, energy/bayes/mdl for multiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 9 (1996) 884-900