Study On Medical Image Compressing And Coding Based On Wavelet Theory

In recent years, medical image compression technology became one of the most important subjects in medical image database research field and worked over throughout the world. The main purpose of medical image compression is to express more clinic information with less data, which can save the storage and transmission time. Because of limitations of the storage space and bandwidth, medical images must be compressed in medical image database system. In last decade, more and more researchers focused on image coding based on wavelet while the wavelet theory research was proceed. Under this background, my research is mainly about the development of novel wavelet based on image coding schemes with special reference to medical images. In this dissertation, some research work in image compression is done.Firstly, the lossless medical image compression methods are compared. And the advantages and disadvantages of each method are particularly analyzed. After applying each method to some of real medical images, we find some shortcomings of each method's the medical image compression methods.Secondly, Proposing a new wavelet image coding method based on predictive quadtree-split. This method hybridizes the intra-band and inter-band coding. Wavelet coefficients in same band are partitioned into many blocks, and the blocks split according to quadtree with the moving of bitplane. This kind of dynamic block splitting can overcome some shortcomings brought by fixed block size. At the same time, the algorithm introduces a predictive coding procedure to truncate the block and make the block's shape fit in with the actual image. A context based arithmetic coder is adopted in entropy coding and four different sets of models are designed for the arithmetic coder. The experiment results show that the proposed algorithm provides better compression.Finally, a new method for construct ion of second generat ion wavelet transform (SGW T ) was introduced in briefly. The characteristics of SGW T are as follow s: It still has the multi resolution nature as first generation wavelet, the construct ion is independent on Fourier transform, the coefficients after transform are integer, the performance of reconstruct ion of image is no t influenced by the boundary conditions. Then, the second generation wavelet transform w as app lied to image lossless coding, according to the characteristic of integertransform of SGW T. SGW T can provide higher compression ratio than Huffman coding while it reconstruct image without losing compared with first generation wavelet transform. The experiment results show the SGW T has exhibited excellent performance in medical image compression coding.