![]() ![]() It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance. 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. In the second stage, the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans. 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. The Rényi entropy is a generalization of the usual concept of entropy which depends on a parameter q. The proposed enhancement model exploits the pixel’s probability representations for image enhancement. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. ![]() sum (DATA (:,i))1,for i1:N, and N is the length of the time series. Usage for all the seven functions: if we assume DATA to be a matrix by MN. the funcition names start with Kq indicate relative entropys. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The functions include extensive Shannon and nonextensive Tsallis,escort Tsallis,and renyi entropy. Due to that, image contrast remains a challenging problem for image segmentation. Many factors like image contrast and quality affect the result of image segmentation. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. A comparative study of the atomic Rnyi entropies for the exponential-cosine screened Coulomb potential (. Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. Other physical quantities have been studied. Received 09 November 2020 Accepted 13 December 2020 Issue published 05 February 2021 (This article belongs to this Special Issue: Recent Advances in Fractional Calculus Applied to Complex Engineering Phenomena)Ĭomputers, Materials & Continua 2021, 67(2), 2061-2075. Ibrahim 3,4,*, Dumitru Baleanu 5,6,7ġ 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 Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI ![]()
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