A Review: ROI based Image Compression of Medical Images

Diseases can be detected using medical imaging methods. One of the problems that physicians encounter with it to store the medical images. This storage occupy more area for storing images long time as there is need to keep the record of numerous patients. So there is need to compress the image to be resolved in a variety of medical images, including radiography, magnetic resonance (MR), mammography, and ultrasound images, X-Rays, Brain MRI, CT images and so on. So this long term rapid transmission is prohibitive without image compression, to reduce the size of files. To make the Medical Images more useful and process able, there is need to reduce the transmission time and storage space for the images. The image may become more visual too, by compressed as it will also help to reduce transmission errors as less data will be transmit, also reduce the cost. In vector quantization technique there is low PSNR values. Further, some methods provide high mean square errors. MAXSHIFT, SPIHT, and General scaling methods having low complexity but provide better results. As DCT (discrete cosines transform) method is widely used in image processing, especially for image compression. DCT algorithm works efficiently to compress the image; it applied on the image blocks. This proposed technique give best performance in terms of computation and speed of computation is high. Furthermore, resulting parameters (such as PSNR, MSE, and entropy) are calculated and compared with existing algorithm and observed that the proposed algorithm has better performance. Keywords— DCT, MAXSHIFT, SPIHT Brain MRI, PSNR, MSE.