Abstract
Despite rapid improvements in storage and data transmission techniques, there is also an increasing need for medical image compression. The advancement in medical imaging systems such as computed tomography (CT), magnetic resonance imaging (MRI), positron emitted tomography (PET), and computed radiography (CR) produces huge amount of volumetric images about various anatomical structure of human body. There exists a need for compression of these images for storage and communication purposes. In this paper we proposed a lossless method of volumetric medical image compression and decompression using a block-based coding technique. The algorithm is tested for different sets of CT colour images using Matlab. The Digital Imaging and Communications in Medicine (DICOM) images are compressed using the proposed algorithm and stored as DICOM formatted images. The inverse nature of the algorithm is used to reconstruct the original image information loss lessly from the compressed DICOM files. We present the simulation results for large set of images to produce a comparative analysis between computational burden and compression ratio for various values of predefined block sizes. This paper finally proves the proposed methodology is better in terms of computational complexity and compression ratio.
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