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Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI

Hamid A. Jalab1, Ala’a R. Al-Shamasneh1, Hadil Shaiba2, Rabha W. Ibrahim3,4,*, Dumitru Baleanu5,6,7

1 Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia
2 Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 84428, Saudi Arabia
3 Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, 758307, Vietnam
4 Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, 758307, Vietnam
5 Department of Mathematics, Cankaya University, Balgat, Ankara, 06530, Turkey
6 Institute of Space Sciences, Magurele-Bucharest, R76900, Romania
7 Department of Medical Research, China Medical University, Taichung, 40402, Taiwan

* Corresponding Author: Rabha W. Ibrahim. Email: email

(This article belongs to this Special Issue: Recent Advances in Fractional Calculus Applied to Complex Engineering Phenomena)

Computers, Materials & Continua 2021, 67(2), 2061-2075.


Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. The proposed enhancement model exploits the pixel’s probability representations for image enhancement. Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels, yielding an overall better details of the kidney MRI scans. In the second stage, the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans. The experimental results showed an average of 95.60% dice similarity index coefficient, which indicates best overlap between the segmented bodies with the ground truth. It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.


Cite This Article

H. A. Jalab, A. R. Al-Shamasneh, H. Shaiba, R. W. Ibrahim and D. Baleanu, "Fractional rényi entropy image enhancement for deep segmentation of kidney mri," Computers, Materials & Continua, vol. 67, no.2, pp. 2061–2075, 2021.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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