Open Access

ARTICLE

Pixel’s Quantum Image Enhancement Using Quantum Calculus

Husam Yahya¹, Dumitru Baleanu^2,3,4, Rabha W. Ibrahim^5,*, Nadia M.G. Al-Saidi⁶

1 Imam Ja’afar Al-Sadiq University, Baghdad, 10064, Iraq
2 Department of Mathematics, Cankaya University, Ankara, 06530, Turkey
3 Institute of Space Sciences, Magurele-Bucharest, R76900, Romania
4 Department of Medical Research, China Medical University, 40402, Taiwan
5 The Institute of Electrical and Electronics Engineers (IEEE), 94086547, Portland, 97005, USA
6 Department of Applied Sciences, University of Technology, Baghdad, 10066, Iraq

* Corresponding Author: Rabha W. Ibrahim. Email:

Computers, Materials & Continua 2023, 74(2), 2531-2539. https://doi.org/10.32604/cmc.2023.033282

Received 13 June 2022; Accepted 26 July 2022; Issue published 31 October 2022

Abstract

The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values. The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone. The brain Magnetic Resonance Imaging (MRI) scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer. Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease. To solve this issue, this research offers a quantum calculus-based MRI image enhancement as a pre-processing step for brain cancer diagnosis. The proposed image enhancement approach improves images with low gray level changes by estimating the pixel’s quantum probability. The suggested image enhancement technique is demonstrated to be robust and resistant to major quality changes on a variety of MRI scan datasets of variable quality. For MRI scans, the BRISQUE “blind/referenceless image spatial quality evaluator” and the NIQE “natural image quality evaluator” measures were 39.38 and 3.58, respectively. The proposed image enhancement model, according to the data, produces the best image quality ratings, and it may be able to aid medical experts in the diagnosis process. The experimental results were achieved using a publicly available collection of MRI scans.

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Keywords

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