Adaptive Smoothing: A General Tool for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Medical image compression with lossless regions of interest
Signal Processing
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Multiple Regions of Interest Image Coding using Compensation Scheme and Alternating Shift
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Exploitation and extension of the region-of-interest coding functionalities in JPEG2000
IEEE Transactions on Consumer Electronics
High performance scalable image compression with EBCOT
IEEE Transactions on Image Processing
Image coding using wavelet transform
IEEE Transactions on Image Processing
Region-of-interest coding based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Medical ultrasound image compression using contextual vector quantization
Computers in Biology and Medicine
VLSI architecture of arithmetic coder used in SPIHT
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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The basic goal of medical image compression is to reduce the bit rate and enhance the compression efficiency for the transmission and storage of the medical imagery while maintaining an acceptable diagnostic image quality. Because of the storage, transmission bandwidth, picture archiving and communication constraints and the limitations of the conventional compression methods, the medical imagery need to be compressed selectively to reduce the transmission time and storage cost along with the preservance of the high diagnostic quality. The other important reason of context based medical image compression is the high spatial resolution and contrast sensitivity requirements. In medical images, contextual region is an area which contains the most useful and important information and must be coded carefully without appreciable distortion. A novel scheme for context based coding is proposed here and yields significantly better compression rates than the general methods of JPEG and JPEG2K. In this proposed method the contextual part of the image is encoded selectively on the high priority basis with a very low compression rate (high bpp) and the background of the image is separately encoded with a low priority and a high compression rate (low bpp) and they are re-combined for the reconstruction of the image. As a result, high over all compression rates, better diagnostic image quality and improved performance parameters (CR, MSE, PSNR and CoC) are obtained. The experimental results have been compared to the scaling, Maxshift, implicit and EBCOT methods on ultrasound medical images and it is found that the proposed algorithm gives better and improved results.