Classification of FTIR cancer data using wavelets and fuzzy C-means clustering

Li Bai; Yihui Liu

doi:10.1117/12.629946

7 November 2005 Classification of FTIR cancer data using wavelets and fuzzy C-means clustering

Li Bai, Yihui Liu

Proceedings Volume 6001, Wavelet Applications in Industrial Processing III; 60010B (2005) https://doi.org/10.1117/12.629946
Event: Optics East 2005, 2005, Boston, MA, United States

Abstract

A feature extracting method based on wavelets for Fourier Transform Infrared (FTIR) cancer data analysis is presented in this paper. A set of low frequency wavelet basis is used to represent FTIR data to reduce data dimension and remove noise. The fuzzy C-means algorithm is used to classify the data. Experiments are conducted to compare classification performance using wavelet features and the original FTIR data provided by the Derby City General Hospital in the UK. Experiments show that only 30 wavelet features are needed to represent 901 wave numbers of the FTIR data to produce good clustering results.

Citation Download Citation

Li Bai and Yihui Liu "Classification of FTIR cancer data using wavelets and fuzzy C-means clustering", Proc. SPIE 6001, Wavelet Applications in Industrial Processing III, 60010B (7 November 2005); https://doi.org/10.1117/12.629946

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