A Novel Approach to ROI based Medical Image Compression using Lifting Wavelets

The process of compressing clinical images is of critical importance in the realm of telemedicine. It is absolutely necessary for remote patient therapy and clinical diagnostics to have images that can be compressed without loss of quality and transmitted quickly. Lossy compression techniques are more efficient than lossless compression techniques when it comes to meeting the storage and transmission requirements of telemedicine. Lossless compression techniques are able to attain a high degree of accuracy. By concentrating on areas of interest, or ROIs, this research hopes to obtain a method of compressing CT scans. The non-ROI regions are compressed using a technique that is based on the lifting component of the Wavelet Transform. The evaluation of a number of factors, including Mean Square Error (MSE), Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), is used to provide experimental results. Both the Compression Ratio (CR) and the Peak Signal to Noise Ratio (PSNR) are shown to have seen significant improvements as a result of the approach that was recommended. This work proposes a solution that is more efficient and effective for compressing CT images in telemedicine applications by utilizing a compression method that involves the region of interest (ROI) as well as lifting wavelet technique.