Efficient volumetric medical image compression using wavelet transform with spiht algorithm

This paper focuses on volumetric medical image compression that operates on three-dimensional (31)) reversible wavelet transform methods. We offer an application of the Set Partitioning in Hierarchical Trees (SPIHT) algorithm to volumetric medical images, using a wavelet image transform and a 3D spatial dependence tree. The wavelet image transform consists of techniques to eliminate computation of certain high-pass coefficients as they are represented by small integer values. Because of the numerical distribution of the high pass coefficients and the effect of the quantization step on small valued coefficients, we can eliminate the high pass coefficients to be zeros and incur minimal image quality loss. This will reduce the computation time for compression of an image. The important properties of the wavelet filter include good localization and symmetric properties, which allow for simple edge treatment, high-speed computation and high quality compressed image. The volumetric medical images are three-dimensional image data sets and can be considered as a sequence of 2D images, or slices. The slices are generally highly correlated with one another, so a transform is used to decorrelate the data and to improve performance of compression efficiency. The most widespread approach for the compression of 3D images combines a three-dimensional decorrelating transform with the extension of a coding algorithm that has proved to be effective on 2D images. The 3D SPHIT coding algorithm developed in this project allows progressive data transmission functionality apart from providing the other advantages such as minimizing computation time and improves the compression ratio and PSNR.