ROI based Medical Image Compression using DWT and SPIHT Algorithm

In an image the spatial region always assumes higher priority compared to other regions. Different image compression methods have the capability of providing higher reconstruction quality of important parts of the image. In case of medical images, small portions of the image may be diagnostically significant, but if incorrectly interpreted, it can have serious ramifications. To circumvent this issue, Region-Based Coding (RBC) is an important technique for transmission and compression of an image that inspires diagnostic significance. Lossless compression schemes possess a safe mechanism of transmission, which in turn play a vital role in telemedicine applications which further research and diagnosis with higher degrees of accuracy. Our research involves employment of lossless scalable RBC for Digital Imaging and Communications in Medicine (DICOM) images by Discrete Wavelet Transform (DWT) and with distortion limiting compression technique for remaining portions of the image. The primary aim of our work is to eradicate the noisy content in the background and resurrect the portions of image in lossless manner. As a part of the Region of Interest (ROI) compression technique, an algorithm is developed using DWT and Set Partitioning in Hierarchical Trees (SPIHT) algorithm. Based on parameters like Mean Square Error (MSE), Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR), we have carried out the entire analysis. ROI compression techniques have outperformed conventional compression schemes as they enhance the quality of ROI with lower MSE and PSNR.