Wavelet-Based Analysis and Classification of Liver CT
Tryphon Lambrou, Linney, AD, Andrew E. Todd-Pokropek · UCL Discovery (University College London) · 2005
In this paper a feasibility study of liver CT dataset classification, using features from different scales of the wavelet transform analysis in conjunction with statistical pattern recognition methods is presented. In our study 720 extracted sub-images from 13 liver CT scans were used, in order to establish which features distinguish better between the normal/cancer classes. Statistical measurements were collected; from the sub-images as well as from their different scale wavelet transform coefficients. We found by using the Leave-One-Out method that the combination of the features from the 1 and 2 Order statistics, achieved overall classification accuracy > 90.0%, both specificity and sensitivity > 90.0%. Features selected by the spatial domain performed better than the wavelet based techniques, under the classification rule of Quadratic Classifier (QC). In addition, features selected by the 3 scale wavelet transform coefficients performed better than those collected from the other wavelet scales, under the classification rule of Bayesian Classifier (BC).