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Browsing by Author "Yahya, Husam"

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    Mathematical design enhancing medical images formulated by a fractal flame operator
    (2022) Baleanu, Dumitru; Yahya, Husam; Mohammed, Arkan J.; Al-Saidi, Nadia M. G.; Baleanu, Dumitru; 56389
    The interest in using fractal theory and its applications has grown in the field of image processing. Image enhancement is one of the feature processing tools, which aims to improve the details of an image. The enhancement of digital pictures is a challenging task due to the unforeseeable variation in the quality of the captured images. In this study, we present a mathematical model using a local conformable differential operator (LCDO). The proposed model is formulated by the theory of cantor fractal to generalize the definition of LCDO. The main advan-tage of utilizing LCDO for image enhancement is its capability to enhance the low contrast intensities using the coefficient estimate of LCDO. The proposed image enhancement algorithm is tested against different images with different qualities to show that it is robust and can withstand dramatic variations in quality. The quantitative results of Brisque, and Piqe were 30.38 and 35.53 respectively. The comparative consequences indicate that the proposed image enhancement model realizes the best i mage quality assessments. Overall, t his model significantly improves the details of the given datasets, and can potentially help the medical staff during the diagnosis process. A MATLAB programming instru-ment utilized for application and valuation of the image quality measures. A comparison with other image techniques is illustrated regarding the visual review. © 2022, Tech Science Press. All rights reserved.
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    Pixel’s Quantum Image Enhancement Using Quantum Calculus
    (2023) Baleanu, Dumitru; Baleanu, Dumitru; Ibrahim, Rabha W.; Al-Saidi, Nadia M.G.; 56389
    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.