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A Metaheuristic Approach for Tetrolet-Based Medical Image Compression

A Metaheuristic Approach for Tetrolet-Based Medical Image Compression
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Author(s): Saravanan S. (Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India) and Sujitha Juliet (Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India)
Copyright: 2022
Volume: 24
Issue: 2
Pages: 14
Source title: Journal of Cases on Information Technology (JCIT)
Editor(s)-in-Chief: Andrew Borchers (Lipscomb University, USA)
DOI: 10.4018/JCIT.20220401.oa3

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Abstract

Over recent times, medical imaging plays a significant role in clinical practices. Storing and transferring the huge volume of images becomes complicated without an efficient image compression technique. This paper proposes a compression algorithm that uses a Haar based wavelet transform called Tetrolet transform, which reduces the noise on the input images and decomposes with a 4 x 4 blocks of equal squares called tetrominoes. It opts for a decomposing using optimal scheme for achieving the input image into a sparse representation which gives a much-detailed performance for texture and edge information better than wavelet transform. Set Partitioning in Hierarchical Trees (SPIHT) is used for encoding the significant coefficients to achieve efficient image compression. It has been investigated with various metaheuristic algorithms. Experimental results prove that the proposed method outperforms the other transform-based compression in terms of PSNR, CR, and Complexity. Also, the proposed method shows an improved result with another state of work.

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