DICOM Image Compression using Huffman Coding Technique with Vector Quantization

Kavinder Kavinder, Vinay Chopra, Harsimranjeet Kaur


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.

Keywords: DICOM (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).

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DOI: https://doi.org/10.26483/ijarcs.v4i3.1555


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