Decorrelation of Sequences of Medical CT Images Based on the Hierarchical Adaptive KLT

In this work is presented one new approach for processing of sequences of medical CT images, called Hierarchical Adaptive Karhunen-Loeve Transform (HAKLT). The aim is to achieve high decorrelation for each group of 9 consecutive CT images, obtained from the original larger sequence. In result, the main part of the energy of all images in one group is concentrated in a relatively small number of eigen images. This result could be obtained using the well-known Karhunen-Loeve Transform (KLT) with transformation matrix of size 9x9. However, for the implementation of the 2-levels HAKLT in each level are used 3 transform matrices of size 3x3, in result of which the computational complexity of the new algorithm is reduced in average 2 times, when compared to that of KLT with 9x9 matrix. One more advantage is that the algorithm permits parallel processing for each group of 3 images in every hierarchical level. In this work are also included the results of the algorithm modeling for sequences of real CT images, which confirm its ability to carry out efficient decorrelation. The HAKLT algorithm could be farther used as a basis for the creation of algorithms for efficient compression of sequences of CT images and for features space minimization in the regions of interest, which contain various classes of searched objects.