In this paper lifting is used for similarity analysis and classification of sets of similar medical images. The lifting scheme is an invertible wavelet transform that maps integers to integers. Lifting provides efficient in-place calculation of transfer coefficients and is widely used for analysis of similar image sets. Images of a similar set show high degrees of correlation with one another. The inter-set redundancy can be exploited for the purposes of prediction, compression, feature extraction, and classification. This research intends to show that there is a higher degree of correlation between images of a similar set in the lifting domain than in the pixel domain. Such a high correlation will result in more accurate classification and prediction of images in a similar set. Several lifting schemes from Calderbank-Daubechies-Fauveue's family were used in this research. The research shows that some of these lifting schemes decorrelates the images of similar sets more effectively than others. The research presents the statistical analysis of the data in scatter plots and regression models.
A wavelet-lifting scheme that maps integers to integers, performs all calculations in-place, and is computationally efficient is used in this paper. It processes 2-D medical images row by row producing an equivalent 1-D signal. The interdependency of pixels in 2-D medical images is known to vary in different regions. Thus, some lifting schemes decorrelate the resultant signal more efficiently than others do. The effect of different scanning approaches on the performance of several lifting schemes is presented.