The image synergetic classification of cancer cells based on prototype modify with rough set methods

Gang Zou; ShuHua Teng; ShiLin Zhou; JiXiang Sun; SenLin Chen

doi:10.1117/12.832798

30 October 2009 The image synergetic classification of cancer cells based on prototype modify with rough set methods

Gang Zou, ShuHua Teng, ShiLin Zhou, JiXiang Sun, SenLin Chen

Proceedings Volume 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 74971H (2009) https://doi.org/10.1117/12.832798
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

The essence of Rough set theory is a mathematic tool describing imperfection and uncertainty, can effectively analyze and deal with those imprecise, inconsistent, incomplete or other imperfect information so as to find out the implied knowledge. The synergetic pattern recognition is a new way of pattern recognition with many excellent features such as noise resistance, deformity resistance, and better robustness. The selection of prototype patterns is very important to pattern recognition of synergetic approach. The main research now is focused on prototype modify from eigenvalue instead of image pixel. Division matrix of rough set can get the best reduce result, and Furthermore dynamic rough set method is applied and optimal non-linear features are got as prototype patterns. Experiment result on cervical squamous intraepithelial cell images shows that the new algorithm can effectively search the optimal prototype patterns, the synergetic recognition method proposed in this paper is more available, and excellent, correct and fast recognition result has been achieved.

Citation Download Citation

Gang Zou, ShuHua Teng, ShiLin Zhou, JiXiang Sun, and SenLin Chen "The image synergetic classification of cancer cells based on prototype modify with rough set methods", Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74971H (30 October 2009); https://doi.org/10.1117/12.832798

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