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Entropy 2018, 20(5), 344;

A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement

Faculty of Computer Science and Information Technology, University Malaya, Kuala Lumpur 50603, Malaysia
Electrical Engineering Department, Assiut University, Assiut 71515, Egypt
Author to whom correspondence should be addressed.
Received: 12 April 2018 / Revised: 2 May 2018 / Accepted: 3 May 2018 / Published: 5 May 2018
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory III)
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Kidney image enhancement is challenging due to the unpredictable quality of MRI images, as well as the nature of kidney diseases. The focus of this work is on kidney images enhancement by proposing a new Local Fractional Entropy (LFE)-based model. The proposed model estimates the probability of pixels that represent edges based on the entropy of the neighboring pixels, which results in local fractional entropy. When there is a small change in the intensity values (indicating the presence of edge in the image), the local fractional entropy gives fine image details. Similarly, when no change in intensity values is present (indicating smooth texture), the LFE does not provide fine details, based on the fact that there is no edge information. Tests were conducted on a large dataset of different, poor-quality kidney images to show that the proposed model is useful and effective. A comparative study with the classical methods, coupled with the latest enhancement methods, shows that the proposed model outperforms the existing methods. View Full-Text
Keywords: local fractional; entropy; MRI; image enhancement local fractional; entropy; MRI; image enhancement

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Al-Shamasneh, A.R.; Jalab, H.A.; Palaiahnakote, S.; Obaidellah, U.H.; Ibrahim, R.W.; El-Melegy, M.T. A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement. Entropy 2018, 20, 344.

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