A Parallel RLE Entropy Coding Technique for DICOM Images on GPGPU

the Electronic Healthcare Record (EHR) is the patient's health care data history, which retains the information about the diseases and their DICOM images reports. This application widely used in the medical field and thereby an employing a Iossless entropy coding technique adaptively to remove the redundancy of an image at a precise level. Run-Length Encoding (RLE) is the basic entropy coding method to adopt in the DICOM images. To accelerate the entropy coding process deployed the parallel architecture CUDA with the GPU technology. The parallelized RLE entropy coding technique implemented on GPGPU platform by using the parallelized CUDA programming. Here we proposed a parallel RLE entropy coding technique to accelerate up to 5X times faster than the serial RLE and 2. 25X times faster than parallel Huffman coding. The parallel RLE runs at O(log N) only.