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.
Highlights
Medical imaging has become one of the most rapidly growing fields in image processing and medical research
It is observed that a Deep Reinforcement Learning algorithm (DRL) [4] is capable of predicting the fractal similarities in an image with appreciably less time in comparison with the classical and Adaptive Iterated Function Systems (IFS) compression schemes [5]
To reduce the time complexity of the classical fractal compression methods, the brute force search is replaced by the Deep Reinforcement Learning system
Summary
Medical imaging has become one of the most rapidly growing fields in image processing and medical research. The first is focussed on the self similarity aspect of medical images, which can help in calculating the affine transformations required to reach a single fixed point. This may help us to achieve a better compression ratio and less decoding time, as promised by the fractal theory. It is observed that a Deep Reinforcement Learning algorithm (DRL) [4] is capable of predicting the fractal similarities in an image with appreciably less time in comparison with the classical and Adaptive IFS compression schemes [5]. The initial idea is to develop an adaptive and efficient Fractal based method, capable of using the self-similarity essence in the human body to compress the respective biomedical images.
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