Medical Image Compression Research Based On Multi-scale Technology

With the development of medical information technology and the prevalence of digital medical treatment, it is necessary to reduce the storage space of medical image, and improve its network transmission speed. The problem how to achieve effective medical image compression is very important. Main research contents are as follows:1) Medical Image Compression Algorithm with Edge Feature Preservation. At low bit rate, reconstructed image has blurred edges when the compression algorithm is implemented by wavelet transform, which is called as Gibbs effect. A new compression algorithm with edge preservation is proposed. First the high frequency edge coefficients are detected. The transformed edge coefficients are elevated during compression code. HVS model is used when we select weight coefficients. That is to say, the weight values are selected according to CSF properties in order to keep the important coefficients. Thus, it can reduce Gibbs effect and improve the visual quality of the image.2) Mixing Code For Medical Image Compression Technique Based On Integer Wavelet Transform. First DPCM forecast transformation is implemented to the medical image, then integer wavelet is done to the medical image. Huffman code and vector quantification are respectively applied to the low frequency coefficients and the high frequency coefficients in the integer wavelet domain. Based on the properties of wavelet coefficients, the major energy of the signal is centerated in the low frequency component. The entropy code is implemented to the low frequency coefficients. Quantification is used to deal with the high frequency coefficients to discard redundant information which is insensitive to the person eyes. Final compressed image is obtained by reconstruction with modified low frequency and high frequency coefficients. The proposed algorithm is compared with the traditional wavelet-based compression algorithm, JPEG and JPEG 2000. Experimental results indicate that high compression ratio and good compression effect can be obtained by the proposed algorithm.3) Multi-ROIS medical image compression with Curvelet and JPEG2000. The curvelet is more suitable for image processing than the wavelet. It is able to represent smooth and edge parts of image with sparsity. In addition, the representation contains more directional information. Based on the features of medical Image and Curvelet, a new multiple arbitrary shape regions-of-interest (ROIS) are proposed. It means to divide the medical image into ROIS and non-ROIS. Different coding algorithms are applied to the .ROIS and non-ROIS. JPEG2000 coding is used for ROIS, and quantizing is used for non-ROIS in Curvelet domain. The experiments are compared with JPEG/JPEG2000. Experimental results show that the proposed algorithm has a low computation burden and high visual quality. It is very useful in Picture Archiving and Communication System (PACS) .