A medical image enhancement based on generalized class of fractional partial differential equations

The interest in using fractional calculus operators has grown in the field of image processing. Image enhancement is one of image processing tools that aims to improve the details of an image. The enhancement of medical images is a challenging task due to the unforeseeable variation in the quality o...

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Main Authors: Ibrahim, Rabha W., Jalab, Hamid Abdullah, Karim, Faten Khalid, Alabdulkreem, Eatedal, Ayub, Mohamad Nizam
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Published: AME Publishing Company 2022
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Online Access:http://eprints.um.edu.my/33552/
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Institution: Universiti Malaya
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spelling my.um.eprints.335522022-08-04T02:14:26Z http://eprints.um.edu.my/33552/ A medical image enhancement based on generalized class of fractional partial differential equations Ibrahim, Rabha W. Jalab, Hamid Abdullah Karim, Faten Khalid Alabdulkreem, Eatedal Ayub, Mohamad Nizam QA75 Electronic computers. Computer science R Medicine Medical technology The interest in using fractional calculus operators has grown in the field of image processing. Image enhancement is one of image processing tools that aims to improve the details of an image. The enhancement of medical images is a challenging task due to the unforeseeable variation in the quality of the captured images. Methods: In this study, we present a mathematical model based on the class of fractional partial differential equations (FPDEs). The class is formulated by the proportional-Caputo hybrid operator (PCHO). Moreover, some properties of the geometric functions in the unit disk are applied to determine the upper bound solutions for this class of FPDEs. The upper bound solution is indicated in the relations of the general hypergeometric functions. The main advantage of FPDF, lies in its capability to enhance the low contrast intensities through the proposed fractional enhanced operator. Results: The proposed image enhancement algorithm is tested against brain and lungs computed tomography (CT) scans datasets of different qualities to show that it is robust and can withstand dramatic variations in quality. The quantitative results of Brisque, Piqe, SSEQ, and SAMGVG were 40.93%, 41.13%, 66.09%, and 31.04%, respectively for brain magnetic resonance imaging (MRI) images and 39.07, 41.33, 30.97, and 159.24 respectively for the CT lungs images. The comparative results show that the proposed image enhancement model achieves the best image quality assessments. Conclusions: Overall, this model significantly improves the details of the given datasets, and could potentially help the medical staff during the diagnosis process. AME Publishing Company 2022-01 Article PeerReviewed Ibrahim, Rabha W. and Jalab, Hamid Abdullah and Karim, Faten Khalid and Alabdulkreem, Eatedal and Ayub, Mohamad Nizam (2022) A medical image enhancement based on generalized class of fractional partial differential equations. Quantitative Imaging in Medicine and Surgery, 12 (1). pp. 172-183. ISSN 2223-4292, DOI https://doi.org/10.21037/qims-21-15 <https://doi.org/10.21037/qims-21-15>. 10.21037/qims-21-15
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
R Medicine
Medical technology
spellingShingle QA75 Electronic computers. Computer science
R Medicine
Medical technology
Ibrahim, Rabha W.
Jalab, Hamid Abdullah
Karim, Faten Khalid
Alabdulkreem, Eatedal
Ayub, Mohamad Nizam
A medical image enhancement based on generalized class of fractional partial differential equations
description The interest in using fractional calculus operators has grown in the field of image processing. Image enhancement is one of image processing tools that aims to improve the details of an image. The enhancement of medical images is a challenging task due to the unforeseeable variation in the quality of the captured images. Methods: In this study, we present a mathematical model based on the class of fractional partial differential equations (FPDEs). The class is formulated by the proportional-Caputo hybrid operator (PCHO). Moreover, some properties of the geometric functions in the unit disk are applied to determine the upper bound solutions for this class of FPDEs. The upper bound solution is indicated in the relations of the general hypergeometric functions. The main advantage of FPDF, lies in its capability to enhance the low contrast intensities through the proposed fractional enhanced operator. Results: The proposed image enhancement algorithm is tested against brain and lungs computed tomography (CT) scans datasets of different qualities to show that it is robust and can withstand dramatic variations in quality. The quantitative results of Brisque, Piqe, SSEQ, and SAMGVG were 40.93%, 41.13%, 66.09%, and 31.04%, respectively for brain magnetic resonance imaging (MRI) images and 39.07, 41.33, 30.97, and 159.24 respectively for the CT lungs images. The comparative results show that the proposed image enhancement model achieves the best image quality assessments. Conclusions: Overall, this model significantly improves the details of the given datasets, and could potentially help the medical staff during the diagnosis process.
format Article
author Ibrahim, Rabha W.
Jalab, Hamid Abdullah
Karim, Faten Khalid
Alabdulkreem, Eatedal
Ayub, Mohamad Nizam
author_facet Ibrahim, Rabha W.
Jalab, Hamid Abdullah
Karim, Faten Khalid
Alabdulkreem, Eatedal
Ayub, Mohamad Nizam
author_sort Ibrahim, Rabha W.
title A medical image enhancement based on generalized class of fractional partial differential equations
title_short A medical image enhancement based on generalized class of fractional partial differential equations
title_full A medical image enhancement based on generalized class of fractional partial differential equations
title_fullStr A medical image enhancement based on generalized class of fractional partial differential equations
title_full_unstemmed A medical image enhancement based on generalized class of fractional partial differential equations
title_sort medical image enhancement based on generalized class of fractional partial differential equations
publisher AME Publishing Company
publishDate 2022
url http://eprints.um.edu.my/33552/
_version_ 1740826042454507520