Çankaya GCRIS Standart veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.
 

Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI

dc.contributor.authorJalab, Hamid A.
dc.contributor.authorAl-Shamasneh, Ala'a R.
dc.contributor.authorShaiba, Hadil
dc.contributor.authorIbrahim, Rabha W.
dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorID56389tr_TR
dc.date.accessioned2022-04-29T12:58:01Z
dc.date.available2022-04-29T12:58:01Z
dc.date.issued2021
dc.departmentÇankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractRecently, 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. © 2021 Tech Science Press. All rights reserved.en_US
dc.identifier.citationJalab, Hamid A...et al. (2021). "Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI", Computers, Materials and Continua, Vol. 67, no. 2, pp. 2061-2075.en_US
dc.identifier.doi10.32604/cmc.2021.015170
dc.identifier.endpage2075en_US
dc.identifier.issn1546-2218
dc.identifier.issue2en_US
dc.identifier.startpage2061en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12416/5462
dc.identifier.volume67en_US
dc.language.isoenen_US
dc.relation.ispartofComputers, Materials and Continuaen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolution Neural Networksen_US
dc.subjectFractional Calculusen_US
dc.subjectMRI Kidney Segmentationen_US
dc.subjectRényi Entropyen_US
dc.titleFractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRItr_TR
dc.titleFractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney Mrien_US
dc.typeArticleen_US
dspace.entity.typePublication

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Article.pdf
Size:
1.1 MB
Format:
Adobe Portable Document Format
Description:
Yayıncı sürümü

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: