A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Screening for oral cancer in its early stage is of utmost importance for improving the survival rate of oral cancer patients. The current method of visual examination followed by biopsy of suspected cases is subjective with inter and intra personal variations. With a low ratio of oral-cancer experts to patients compounded by the reluctance of patients to undergo biopsy in rural India. The situation cries out for automatic screening device for oral cancer. In this context, optical spectroscopy based on Laser Induced Fluorescence (LIF) has been shown to be a promising technique in distinguishing between cancerous and benign lesions in the mouth. However, it has been observed that it is very difficult to distinguish pre-malignant spectra from malignant and normal spectra recorded in-vivo. Hence, obtaining the most discriminating features from the spectra becomes important. In this article a new method of feature selection is proposed using mean-shift and Recursive Feature Elimination (RFE) techniques to increase discrimination ability of the feature vectors. Performance of the algorithm is evaluated on a in-vivo recorded LIF data set consisting of spectra from normal, malignant and pre-malignant patients. Sensitivity of above 95% and specificity of above 99% towards malignancy are obtained using the proposed method.