Mammographic image based breast tissue classification with kernel self-optimized fisher discriminant for breast cancer diagnosis

Journal of Medical Systems

Volumn 36, Issue 4, 2012, Pages 2235-2244

  Li, Jun Bao ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Breast cancer; Breast tissue classification; Connected component labeling; Kernel self optimized fisher discriminant

Indexed keywords

ARTICLE; ARTIFACT; BREAST; BREAST CANCER; CANCER DIAGNOSIS; CLASSIFICATION ALGORITHM; HUMAN; HUMAN TISSUE; KERNEL METHOD; MAMMOGRAPHY; PARENCHYMA; PECTORAL MUSCLE; ALGORITHM; BREAST TUMOR; CLASSIFICATION; COMPUTER ASSISTED DIAGNOSIS; DISCRIMINANT ANALYSIS; FEMALE; IMAGE PROCESSING; METHODOLOGY; RADIOGRAPHY;

ALGORITHMS; BREAST NEOPLASMS; DIAGNOSIS, COMPUTER-ASSISTED; DISCRIMINANT ANALYSIS; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAMMOGRAPHY;

EID: 84873034340     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-011-9691-4     Document Type: Article

References (25)

1. Wang, X. H., Good, W. F., Chapman, B. E., Chang, Y. H., Poller, W. R., Chang, T. S., and Hardesty, L. A., Automated assessment of the composition of breast tissue revealed on tissue-thickness-corrected mammography. Am. J. Roentgenol. 180:227-262, 2003.
2. Muhimmah, I., and Zwiggelaar, R., "Mammographic density classification using multiresolution histogram information", Proceedings of the International Special Topic Conference on Information Technology in Biomedicine, Ioannina, Greece, 2006
3. Oliver, A., Freixenet, J., Marti, R., Pont, J., Perez, E., Denton, E. R. E., and Zwiggelaar, R., A novel breast tissue density classification methodology. IEEE Trans. Inf. Technol. Biomed. 12(1):55-65, 2008.
4. Li, J. B., Pan, J. S., and Lu, Z. M., Kernel optimization-based discriminant analysis for face recognition. Neural Comput. Appl. 18(6):603-612, 2009.
5. Suckling, J., Parker, J., Dance, D., Astley, S., Astley, I., Hutt, I., and Boggis, C., The mammographic images analysis society digital mammogram database. Exerpta Med. 1069:375-378, 1994.
6. Li, J.-B., Chu, S.-C., Pan, J.-S., and Ho, J.-H., Adaptive data-dependent matrix norm based Gaussian kernel for facial feature extraction. Int. J. Innovative Comput. Inf. Control. 3(5):1-10, 2007.
7. Sahbi, H., Kernel PCA for similarity invariant shape recognition. Neurocomput 70:3034-3045, 2007.
8. Wu, M., Schölkopf, B., and BakIr, G., A direct method for building sparse kernel learning algorithms. J. Mach. Learn Res. 7:603-624, 2006.
9. Mika, S., Ratsch, G., Weston, J., Schölkopf, B., and Muller, K.-R., Fisher discriminant analysis with kernels, in: Proceedings of IEEE International Workshop on Neural Networks for Signal Processing IX, pp. 41-48, 1999
10. Schölkopf, B., Smola, A., and Muller, K. R., Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5):1299-1319, 1998.
11. Tao, D., Tang, X., Li, X., and Wu, X., Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 28(7):1088-1099, 2006.
12. Lu, J., Plataniotis, K. N., and Venetsanopoulos, A. N., Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans. Neural Netw. 14(1):117-226, 2003.
13. Yang, J., Frangi, A. F., Yang, J.-Y., Zhang, D., and Jin, Z., KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27(2):230-244, 2005.
14. Yeung, D.-Y., Chang, H., and Dai, G., Learning the kernel matrix by maximizing a KFD-based class separability criterion. Pattern Recogn. 40(7):2021-2028, 2007.
15. Huang, J., Yuen, P. C., Chen, W.-S., and Lai, J. H., Kernel subspace LDA with optimized kernel parameters on face recognition, in: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004.
16. Chen, W.-S., Yuen, P. C., Huang, J., and Dai, D.-Q., Kernel machine-based one-parameter regularized fisher discriminant method for face recognition. IEEE Trans. Syst. Man. Cybern. B Cybern. 35(4):658-669, 2005.
17. Amari, S., and Wu, S., Improving support vector machine classifiers by modifying kernel functions. Neural Netw. 12(6):783-789, 1999.
18. Pan, J. S., Li, J. B., and Lu, Z. M., Adaptive quasiconformal Kernel discriminant analysis. Neurocomputing 71:2754-2760, 2008.
19. Mohamad, M. S., Omatu, S., Yoshioka, M., and Deris, S., A three-stage method to select informative genes for cancer classification. Int. J. Innovative Comput. Inf. Control 6(1):117-126, 2010.
20. Mohamad, M. S., Omatu, S., Yoshioka, M., and Deris, S., A cyclic hybrid method to select a smaller subset of informative genes for cancer classification. Int. J. Innovative Comput. Inf. Control 5(8):2189-2202, 2009.
21. Mohamad, M. S., Omatu, S., Yoshioka, M., and Deris, S., A two-stage method to select a smaller subset of informative genes for cancer classification. Int. J. Innovative Comput. Inf. Control 5(10):2959-2968, 2009.
22. Pan, M., Tang, J., and Yang, X., An algorithm for medical image tilt correction using B-Spline and moment invariants. ICIC Express Lett. 4(1):57-64, 2010.
23. Zhou, C., Wei, X., Zhang, Q., and Wang, B., Image encryption based on DNA splicing system and henon model. ICIC Express Lett. 4(3B):1011-1018, 2010.
24. Arora, S., Bhattacharjee, D., Nasipuri, M., Basu, D. K., and Kundu, M., Complementary features combined in a MLP-based system to recognize handwritten devnagari character. J. Inf. Hiding Multimedia Signal Process. 2(1):71-77, 2011.
25. Parviz, M., and Shahram Moin, M., Boosting approach for score level fusion in multimodal biometrics based on AUC maximization. J. Inf. Hiding Multimedia Signal Process. 2(1):51-59, 2011.