Choquet Integral based Feature Selection for Early Breast Cancer Diagnosis from MRIs

Soumaya Trabelsi Ben Ameur, Florence Cloppet, Dorra Sellami Masmoudi, Laurent Wendling


This paper focuses on breast cancer of the mammary gland. Both basic segmentation steps and usual features are recalled. Then textural and morphological information are combined to improve the overall performance of breast MRI in a computer-aided system. A model of selection based on Choquet integral is provided. Such model is suitable when handling with a weak amount of data even ambiguous in some extent. Achieved results compared to well-known classification methods show the interest of our approach.


  1. Albregtsen, F., 2008. Statistical Texture Measures Computed from Gray Level Co-occurrence Matrices. Image Processing Laboratory Department of Informatics University of Oslo.
  2. Chen, W., Giger, ML., Bick, U., 2006. A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic ContrastEnhanced MR Images. Academic Radiology. Volume 13, issue 1, pages 63-72.
  3. Chen, W., Giger, ML., Bick, U., 2006. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys. 33(8), pages 2878-87.
  4. Chen, W., Giger, ML., Lan, L., et al., 2004. Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med Phys. Volume 31, pages 1076-1082.
  5. Clausi, DA., 2002. An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sensing. Volume 28, issue 1, pages 45-62.
  6. Duda, RO., Hart, PE., Stork, DG., 2012. Pattern classification. John Wiley & Sons.
  7. Grabisch, M., 1996. The application of fuzzy integrals in multicriteria decision making. Eur. J. Oper. Res. Volume 89, issue 3, pages 445-456.
  8. Haralick, RM., Shanmuga, K., Dinstein, I., 1973. Textural features for image classification. IEEE Trans Syst Man Cybern; Smc3 (6):610-21.
  9. Hwang, S.K., Kim, W.Y., 2006. A novel approach to the fast computation of Zernike moments. Pattern Recognition. Volume 39 pages 2065-2076.
  10. Karthikeyan, S., Rengarajan, N., 2014. Performance analysis of gray level co-occurrence matrix texture features for glaucoma diagnosis. American Journal of Applied Sciences. Volume 11, pages 248-257.
  11. Li, C., Kao, CY., Gore, JC., Ding, Z., 2008. Minimization of region-scalable fitting energy for image segmentation. IEEE Transactions on Image Processing. Volume 17, issue 10, pages 1940-1949.
  12. Li, C., Huang, R., Ding, Z., Gatenby, JC., Metaxas, DN., Gore, JC., 2011. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Transactions on image processing. Volume 20, issue 7.
  13. Li, Sh., Lee, MCh, Pun, Ch.M., 2009. Complex Zernike moments features for shape based image retrieval. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 1. Volume 39, pages 227- 237.
  14. Li, X., Li, L., Lu, H., Chen, D., Liang, Z., 2003. Inhomogeneity Correction for Magnetic Resonance Images with Fuzzy C-Mean Algorithm. Proc. SPIE Int. Soc. Opt. Eng., Volume 5032, pages 995-1005.
  15. Li, X., Song, A., 2010. A new edge detection method using Gaussian-Zernike moment operator. Proceedings of the IEEE, 2nd International Asia Conference on Informatics in Control, Automation and Robotics, pages 276-279.
  16. Liney, GP., Sreenvias, M., Garcia-Alvarez, R., et al., 2006. Breast lesion analysis of shape technique: semiautomated vs. manual morphological description. J Magnetic Resonance Imaging. Volume 23, pages 493- 498.
  17. Mazaud C., Rendek J., Bombardier V., Wendling L., "A feature selection method based on Choquet Integral and Typicality Analysis", IEEE International Conference on Fuzzy Systems, London (UK), 6p, 2007.
  18. Murofushi, T., Sugeno, M., 1991. A theory of fuzzy measures: Representations, the Choquet integral, and null sets. J. Math. Anal. Appl. Volume 159, issue 2, pages 532-549.
  19. Murofushi, T., Soneda, S., 1993. Techniques for reading fuzzy measures (iii): Interaction index. Proc. 9th Fuzzy Syst. Symp., Sapporo, Japan. Pages 693-696.
  20. Nie, K., 2008. Quantitative Analysis of Lesion Morphology and Texture Features for Diagnostic Prediction in Breast MRI. Academic Radiology, volume 15, issue 12, pages 1513-1525.
  21. Shapley, L., 1953. A value for n-person games. In Contributions to the Theory of Games, Annals of Mathematics Studies, H. Khun and A. Tucker, Eds. Princeton, NJ: Princeton Univ. Press, pages 307-317.
  22. Soh, LK., Tsatsoulis, C., 1999. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on Geoscience and Remote Sensing. Volume 37, issue 2, pages 780-795.
  23. Tahmasbi, A., 2011. Classification of benign and malignant masses based on Zernike moments. Computers in Biology and Medicine. Volume 41, pages 726-735.
  24. Wang, TC., Huang, YH., Huang, CS., Chen, JH., Huang, GY., Chang, YC., Chang, RF., 2014. Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis. Magnetic Resonance Imaging. Volume 32, pages 197-205.
  25. Wendling L., Rendek J., Matsakis P., 2008. Selection of suitable set of decision rules using Choquet integral, Statistic Pattern Recognition, S+SSPR'08, pp10.
  26. Winzenrieth, R., Claude, I., Hobatho, MC., Pouletaut, P., Sebag, G., 2003. Comparaison de deux m├ęthodes de segmentation par contours actifs : les snakes et les level sets pour la segmentation d'IRM de hanche.
  27. Zhang, D., Lu, G., 2002. Generic Fourier descriptor for shape-based image retrieval. Multimedia and Expo, ICME 7802. Proceedings. IEEE International Conference. Volume1, pages 425-428.
  28. Zhang, DQ., Chen, S., Pan, ZS., Tan, KR., 2003. Kernel based fuzzy clustering incorporating spatial constraints for image segmentation. IEEE International conference on Machine Learning and Cybernetics. Volume 4, pages 2189-2192.

Paper Citation

in Harvard Style

Trabelsi Ben Ameur S., Cloppet F., Sellami Masmoudi D. and Wendling L. (2016). Choquet Integral based Feature Selection for Early Breast Cancer Diagnosis from MRIs . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 351-358. DOI: 10.5220/0005754703510358

in Bibtex Style

author={Soumaya Trabelsi Ben Ameur and Florence Cloppet and Dorra Sellami Masmoudi and Laurent Wendling},
title={Choquet Integral based Feature Selection for Early Breast Cancer Diagnosis from MRIs},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Choquet Integral based Feature Selection for Early Breast Cancer Diagnosis from MRIs
SN - 978-989-758-173-1
AU - Trabelsi Ben Ameur S.
AU - Cloppet F.
AU - Sellami Masmoudi D.
AU - Wendling L.
PY - 2016
SP - 351
EP - 358
DO - 10.5220/0005754703510358