Mass screening and feature reserved compression in a computer-aided system for mammograms

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2008, Pages 3779-3786

  Yang, Sheng Chih ^a   Lin, Yi Jhen ^a   Chung, Pau Choo ^b   Hsu, Giu Cheng ^c   Lo, Chien Shen ^d  

^a NATIONAL CHIN YI UNIVERSITY OF TECHNOLOGY   (Taiwan)

^b NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^c TRI SERVICE GENERAL HOSPITAL   (Taiwan)

^d NATIONAL FORMOSA UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA COMPRESSION; HOPFIELD NEURAL NETWORKS; VECTOR QUANTIZATION; VEGETATION;

COMPETITIVE HOPFIELD NEURAL NETWORKS; COMPRESSION RATES; COMPUTER-AIDED SYSTEMS; FEATURE SUBSETS; IMPORTANCE FACTORS; MASS REGIONS; PRESCREENING; PROGRESSIVE COMPRESSIONS; STORAGE EFFICIENCIES; STORAGE SYSTEMS; SUB SYSTEMS; SVM CLASSIFIERS; TEXTURE FEATURES;

NEURAL NETWORKS;

EID: 56349160171     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4634341     Document Type: Conference Paper

References (13)

1. M. Sameti, R.K. Ward, B. Palcic and J. Morgan-Parkes, "Texture feature extraction, for tumor detection in mammographie images," IEEE Pacific Rim Conference on Communications, Computers and Signal Processing 2, pp. 831-834, 1997.
2. K. Bovis and S. Singh, "Detection of masses in mammograms using texture features," Proceedings of 15th International Conference on Pattern Recognition 2, pp. 267-270, 2000.
3. J. J. Hopfield and D. W. Tank, "Neural computation of decisions in optimization problems," Biol. Cybern., Vol. 52, pp. 141-152, 1985.
4. th IPPR Coferenceon Computer Vision, Graphics and Image Processing, pp.362-369, 2000.
5. H. C. Huang and J. L. Wu, "Grey System Theory on Image Processing and Lossless Data Compression for HD-media," The Journal of Grey Theory & Practice, vol.3, no.2, pp.9-15, 1993.
6. Y. T. Hsu and J. Yeh, "Grey-Base Image Compression," The Journal of Grey System, vol.10, no. 2, pp.105-120, 1998.
7. P. Pudil, F.J. Ferri, J. Novovicova and J. Kittler, "Floating search methods for feature selection with nonmonotonic criterion functions," Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol. 2, pp. 279-283, 1994.
8. P. Pudil, K. Fuka, K. Beranek and P. Dvorak, "Potential of artificial intelligence based feature selection methods in regression, models," Proceedings of Computational Intelligence and Multimedia Applications, ICCIMA '99, pp. 159-163, 1999.
9. S.J. Reeves and Z. Zhe, "Sequential Algorithms for Observation Selection," IEEE Transaction on Signal Processing, vol. 47, no. 1, pp. 123-132, 1999.
10. C.J.C. Burges, "A tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery 2, pp. 121-167, 1998.
11. C. Campbell, "Algorithmic Approaches to Training Support Vector Machines: A Survey," ESANN'2000 proceedings, pp. 27-36, 2000.
12. http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html
13. C.C. Chang, and C.J. Lin, LIBSVM: a library for support vector machines, software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm, (2005).