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Proceedings Paper

Classification of osteosarcoma T-ray responses using adaptive and rational wavelets for feature extraction
Author(s): Desmond Ng; Fu Tian Wong; Withawat Withayachumnankul; David Findlay; Bradley Ferguson
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Paper Abstract

In this work we investigate new feature extraction algorithms on the T-ray response of normal human bone cells and human osteosarcoma cells. One of the most promising feature extraction methods is the Discrete Wavelet Transform (DWT). However, the classification accuracy is dependant on the specific wavelet base chosen. Adaptive wavelets circumvent this problem by gradually adapting to the signal to retain optimum discriminatory information, while removing redundant information. Using adaptive wavelets, classification accuracy, using a quadratic Bayesian classifier, of 96.88% is obtained based on 25 features. In addition, the potential of using rational wavelets rather than the standard dyadic wavelets in classification is explored. The advantage it has over dyadic wavelets is that it allows a better adaptation of the scale factor according to the signal. An accuracy of 91.15% is obtained through rational wavelets with 12 coefficients using a Support Vector Machine (SVM) as the classifier. These results highlight adaptive and rational wavelets as an efficient feature extraction method and the enormous potential of T-rays in cancer detection.

Paper Details

Date Published: 5 January 2008
PDF: 13 pages
Proc. SPIE 6802, Complex Systems II, 680211 (5 January 2008); doi: 10.1117/12.753026
Show Author Affiliations
Desmond Ng, The Univ. of Adelaide (Australia)
Fu Tian Wong, The Univ. of Adelaide (Australia)
Withawat Withayachumnankul, The Univ. of Adelaide (Australia)
David Findlay, The Univ. of Adelaide (Australia)
Bradley Ferguson, The Univ. of Adelaide (Australia)


Published in SPIE Proceedings Vol. 6802:
Complex Systems II
Derek Abbott; Tomaso Aste; Murray Batchelor; Robert Dewar; Tiziana Di Matteo; Tony Guttmann, Editor(s)

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