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DOI: 10.14569/IJACSA.2021.0120492
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Fast Fractal Coding of MRI Images using Deep Reinforcement Learning

Author 1: Bejoy Varghese
Author 2: S. Krishnakumar

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 4, 2021.

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Abstract: This paper presents an algorithm based on Fractal theory by using Iterated Function Systems (IFS). An efficient and fast coding mechanism is proposed by exploiting the self similarity nature in the Brain MRI images. The proposed algorithm utilizes Deep Reinforcement Learning (DRL) technique to learn the transformations required to recreate the original image. We avail of the Adaptive Iterated Function System (AIFS) as the encoding scheme. The proposed algorithm is trained and customized to compress the Medical images, especially Magnetic Resonance Imaging (MRI). The algorithm is tested and evaluated by using the original MR head scan test images. It learns from an existing biomedical dataset viz The Internet Brain Segmentation Repository (IBSR) to predict the new local affine transformations. The empirical analysis shows that the proposed algorithm is at least 4 times faster than the competitive methods and the decoding quality is far distinct with a reduction in the bit rate.

Keywords: Fractal compression; deep reinforcement learning; MRI image compression; deep learning; adative fractal coding

Bejoy Varghese and S. Krishnakumar, “Fast Fractal Coding of MRI Images using Deep Reinforcement Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 12(4), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120492

@article{Varghese2021,
title = {Fast Fractal Coding of MRI Images using Deep Reinforcement Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120492},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120492},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {4},
author = {Bejoy Varghese and S. Krishnakumar}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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