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.