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It consists in two steps: the high pass called The 4D medical image is a succession of volumes in predict step and the low pass called update step. In our method we use a 2D integer wavelet transform The first step of our scheme is the reorganization of the slices, it consists to representing 4D image by N Group of achieved by the scheme lifting for three decomposition Slices GOS and each GOS is composed by M 2D slices.

For example if 4D image is composed by 4 volumes then C. While the proposed scheme applies all other GOS will be achieved by continuing to browse the rest of the 4D image Fig. B: predicted slice. The H: high-pass slice or residual slice because it represents wavelets transform produce a hierarchical representation prediction error obtained between predicted slice B and by applying successive levels of decomposition along reference slices A and C.

Thus the slice 9 is used as the first slice in the achieving 2D integer wavelet transform in spatial next GOS. Figure 3. The SPIHT algorithm has been spread to the case 3D for Among the three errors the minimum error is chosen the video [12] and for the compression of volumetric well as the prediction method for pixel m, n of the images by using a 3D wavelet transform. The principles predicted slice B. The slice 9 is III. The result slices reference and In our scheme after applying the 2D integer wavelet predicted slices are coded with SPIHT3D and the motion transform on all slices constituting the GOS, the obtained vectors are entropy coded.

The We adopt block motion compensation model with result slices are coded with SPIHT3D, the motion vectors simple full search method to find the most similar blocks and the etiquettes are entropy coded.

However more pixel accuracy can be used in independently by using 3D integer wavelet transform, the proposed scheme. The difference between Table I summarizes the results of the lossless the location of predicted block and the location of the compression which are compared by compression rate in most similar block is the motion vector. Kassim, P. Yan, W. Lee, and K. Yan and A. Sanchez, P. Nasiopoulos, and R. Yea and W. In Table II we present the results obtained by using the [7] J. Choi and J.

The [9] A. Secker and D. Conference on Image Processing, , pp. Having issues? We can help. Visit our Support page for documentation and videos, or contact us directly.

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