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

Ibrahim, Rabha W. and Jalab, Hamid A. 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.

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Background: 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.

Item Type: Article
Funders: Universiti Malaya, Malaysia (GPF096C-2020)
Uncontrolled Keywords: Fractional calculus; Differential operator; Univalent function; Analytic function; Image processing; Image enhancement; Medical images
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Computer System & Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 23 Feb 2022 02:29
Last Modified: 23 Feb 2022 07:12
URI: http://eprints.um.edu.my/id/eprint/26349

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