Image Compression of medical images using VQ-Huffman Coding Technique

Digital Medical Imaging has grown very fast in recent years and hence plays a vital role in diagnosis, treatment, and research area. All the radiological modalities such as CT scanners, MRI, US, PET, X-Ray made by multiple vendors and located at one or many sites can communicate by means of DICOM across an network. Now days, hospitals need to store large volume of data about the patients that require huge hard disk space and high bandwidth. This would employ the need to compress DICOM images for efficient storage and transmission over the internet. In this paper, a new compression algorithm combining the features of both lossy (DCT) and lossless (Huffman Coding) compression techniques has been designed and implemented. The performance of proposed algorithm is then improved using Vector Quantization technique in the context of increasing Compression Ratio as well as preserving the quality of compressed images. Different quality metrics like MSE, PSNR and CR are computed on various medical test images. The experimental results show that proposed compression technique performs better than the existing techniques in terms of performance parameters. KeywordsDICOM (Digital Imaging and Communication in medicine), DCT (Discrete Cosine Transform), Huffman Coding, Vector Quantization, PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error) and CR (Compression Ratio).