Image fusion for following-up brain tumor evolution

Proceedings - International Symposium on Biomedical Imaging

Volumn , Issue , 2011, Pages 281-284

  Ruan, Su ^a   Zhang, Nan ^a,b,c   Liao, Qingmin ^b   Zhu, Yuemin ^c  

^a UNIVERSITÉ DE ROUEN   (France)

^b TSINGHUA UNIVERSITY   (China)

^c CNRS   (France)

Author keywords

feature selection; follow up system; image fusion; Support Vector Machine (SVM); tumor segmentation

Indexed keywords

BRAIN TUMORS; DATA FUSION METHODS; DATA SEQUENCES; FEATURE SELECTION METHODS; FOLLOW UP; FOLLOW-UP SYSTEM; MULTI-SPECTRAL; QUANTITATIVE COMPARISON; SUPPORT VECTOR; THERAPEUTIC TREATMENTS; TUMOR SEGMENTATION; TUMOR TISSUES;

FEATURE EXTRACTION; HISTOLOGY; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; PATIENT TREATMENT; TUMORS;

IMAGE FUSION;

EID: 80055058257     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2011.5872406     Document Type: Conference Paper

References (13)

1. D.L. Phamé, C.Y. Xue, and J.L. Prince, "A survey of current methods in medical image segmentation", Annual review of biomedical engineering, vol.2 (2000), pp.315-338, 2001;
2. S. Ruan, S. Lebonvallet, A. Merabet, and J.M. Constans, "Tumor segmentation from a multi-spectral MRI images by using support vector machine classification", 4th IEEE Int. Symp. Biomedical Imaging (ISBI'07), pp.1236-1239, 2007;
3. E. Leopold and J. Kindermann, "Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?" Machine Learning, vol. 46, pp.423-444, 2002;
4. Y. Rui, T.S. Huang, and S. Chang, "Image Retrieval: Current Techniques, Promising Directions and Open Issues," Visual Comm. and Image Representation, vol. 10 (4), pp.39-62, 1999;
5. E. Xing, M. Jordan, and R. Karp, "Feature Selection for High-Dimensional Genomic Microarray Data," Proc. 15th Int'l Conf. Machine Learning, pp.601-608, 2001;
6. T. Jolliffe, "Principal Component Analysis", New York: Springer, pp.151-156, 1986;
7. B. Scholkopf, E. Smola, and K.R. Muller, et al, "Nonlinear Component Analysis as a Kernel Eigenvalue Problem," Neural Computation, vol.10 (5), pp.1299-1319, 1998;
8. L. Wang, "Feature Selection with Kernel Class Separability". IEEE Transactions on PAMI, vol.30 (9), pp.1534-1546, 2008;
9. K. Polat, S. Güneş, "A new feature selection method on classification of medical datasets: Kernel F-score feature selection", Expert Systems with Applications, vol. 36(7), pp.10367-10373, 2009;
10. M.D. Shieh and C.C. Yang, "Multiclass SVM-RFE for product form feature selection", Expert Systems with Applications: An International Journal, vol.35 (1-2), pp.531-541, 2008;
11. Y. LeCun, J. Denker, and S. Solla, et al, "Optimal brain damages", Advances in neural information processing systems, CA, Mateo: Morgan, Kaufman, pp.598-605, 1990;
12. B. Calvo, P. Larrañaga, and J.A. Lozano, "Feature Subset Selection from Positive and Unlabelled Examples", Pattern Recognition Letters, vol.30(11), pp.1027-1036, 2009;
13. C.C. Chang and C.J. Lin, "LIBSVM - A Library for Support Vector Machines", http://www.csie.ntu.edu.tw/~cjlin/libsvm/, 2010.