Fast Fractal Coding of MRI Images using Deep Reinforcement Learning

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...

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Bibliographic Details
Published inInternational journal of advanced computer science & applications Vol. 12; no. 4
Main Authors Varghese, Bejoy, Krishnakumar, S.
Format Journal Article
LanguageEnglish
Published West Yorkshire Science and Information (SAI) Organization Limited 2021
Subjects
Affine transformations
Algorithms
Brain
Decoding
Deep learning
Empirical analysis
Fractals
Image coding
Image segmentation
Machine learning
Magnetic resonance imaging
Medical imaging
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Summary: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.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2021.0120492