A review on region of interest-based hybrid medical image compression algorithms

Digital medical images have become a vital resource that supports decision-making and treatment procedures in healthcare facilities. The medical image consumes large sizes of memory, and the size keeps on growth due to the trend of medical image technology. The technology of telemedicine encourages the medical practitioner to share the medical image to support knowledge sharing to diagnose and analyse the image. The healthcare system needs to ensure distributes the medical image accurately with zero loss of information, fast and secure. Image compression is beneficial in ensuring that achieve the goal of sharing this data. The region of interest-based hybrid medical compression algorithm plays the parts to reduce the image size and shorten the time of medical image compression process. Various studies have enhanced by combining numerous techniques to get an ideal result. This paper reviews the previous works conducted on a region of interest-based hybrid medical image compression algorithms

Lossless Compression Methods for Real-Time Images

This paper proposes and implements two lossless methods, to compress real-time greyscale medical images, which are Huffman coding and a new lossless method called Reduced Lossless Compression Method (RLCM), both of which were tested when applying a random sample of greyscale medical images with a size of 256×256 pixels. Different factors were measured to check the compression method performances such as the compression time, the compressed image size, and the compression ratio (CR). The system is fully implemented on a field programmable gate array (FPGA) using a fully hardware based (no software driven processor) system architecture. A Terasic DE4 board was used as the main platform for implementing and testing the system using Quartus-II software and tools for design and debugging. The impact of compressing the image and carrying the compressed data through parallel lines is like the impact of compressed the same image inside a single core with a higher compression ratio, in this system between 7.5 and 126.8