Research On Medical Image Compression Coding Algorithm Based On Vector Quantization

Medical image has characteristics of large scale, high resolution and a large volume of data. Especially, it will lead to misdiagnoses that information of the region of diagnostic interest (ROI) is lost in image data compression. People are searching for appropriate data compression methods are to represent images efficiently. This achievement would allow data storage at relatively low cost and data transmission at relatively high speed, both of which are essential in the design of picture archiving and communication systems..We put forward a medical image compression coding algorithm, that is, the quality of the focus region is guaranteed and other regions are compressed at high ratio.The algorithm adopts lossless compression in the region of diagnostic interest (ROI), and lossy compression in other region (background). In the process of compressing the ROI, to decrease the complexity of computation and ensure the quality of the image, integer wavelets transform and arithmetic algorithm are used. We develop a Wavelets-VQ method to compress the background region. Before quantifying, the wavelet coefficients should be given a vision weight, and then organized in the way of across different subbands according to the same direction.To accelerate the convergence speed of the codebook training and ensure the quality of the compression, two key techniques of vector quantization are improved. On the other hand, a fast codeword search algorithm based on wavelet transform is put forward to expedite codeword search process.At last, algorithm is validated in the C++ and matlab. Experiment results show that, satisfying compression outcomes are gained at the expense of little increase of computation.