Medical Image Compression Utilizing The Serial Differences and ‎Coding Techniques

Ghalib Ahmed Salman; Ahmed Ahmed; HAREER MOAIAD  HUSSEN

doi:10.51173/ijds.v2i1.13

Authors

Ghalib Ahmed Salman Electrical Engineering Technical College, Middle Technical University, Iraq https://orcid.org/0009-0009-3759-5734
Ahmed G. Ahmed Electrical Engineering Technical College, Middle Technical University, Iraq
HAREER MOAIAD Electrical Engineering Technical College, Middle Technical University, Iraq

DOI:

https://doi.org/10.51173/ijds.v2i1.13

Keywords:

Serial Medical Images, Huffman Coding, Run Length Encoding, near-Lossless compression, image compression

Abstract

Different medical devices for imaging used by centers and clinics produce an increasing number of sequential medical images. ‎Different imaging techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Fluoroscopy ‎produce a set of series for the same patient. Within these images, most of the image parts are fixed against noticeable changes ‎in the remaining part. This consumes non-ignorable storage space. This paper proposes a near-lossless compression ‎technique that considers the fixed image parts to focus on changing parts for a higher compression ratio. In some applications, lossless compression techniques are highly preferable against preferring lossy techniques in some applications. In other applications, near-lossless compression techniques are preferable to lossless and lossy compression techniques, where lossy ones may ‎lose significant details, and the lossless ones produce less compression ratios than near-lossless ones. Previous works dealt with Fluoroscopy images as individual images or using ‎video compression techniques. This work tends to handle the whole series of ‎images as an integrated object. This paper considers subtracting successive ‎images to detect ROI areas producing zero overall values over similar ‎areas and non-zero ones within ROI ones. The double coding technique and near-lossless concept of compression increase the compression ratio. ‎Conducted experiments showed encouraging results benchmarking the other published ‎works in medical image compression.‎

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