Open Access

ARTICLE

Mathematical Design Enhancing Medical Images Formulated by a Fractal Flame Operator

by Rabha W. Ibrahim^1,*, Husam Yahya², Arkan J. Mohammed³, Nadia M. G. Al-Saidi⁴, Dumitru Baleanu^5,6,7

1 Institute of Electrical and Electronics Engineers (IEEE): 94086547, Kuala Lumpur, 59200, Malaysia
2 Imam Ja'far Al-Sadiq University, Baghdad, 10064, Iraq
3 Department of Mathematics, College of Science, Mustansiriyah University, Baghdad, 10052, Iraq
4 Department of Applied Sciences, University of Technology, Baghdad, 10066, Iraq
5 Department of Mathematics, Cankaya University, Ankara, 06530, Turkey
6 Institute of Space Sciences, Magurele-Bucharest, R76900, Romania
7 Department of Medical Research, China Medical University, Taichung, 40402, Taiwan

* Corresponding Author: Rabha W. Ibrahim. Email:

(This article belongs to the Special Issue: New Trends in Artificial Intelligence and Deep learning for Instrumentation, Sensors, and Robotics)

Intelligent Automation & Soft Computing 2022, 32(2), 937-950. https://doi.org/10.32604/iasc.2022.021954

Received 21 July 2021; Accepted 25 August 2021; Issue published 17 November 2021

Abstract

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 advantage 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 image quality assessments. Overall, this model significantly improves the details of the given datasets, and can potentially help the medical staff during the diagnosis process. A MATLAB programming instrument utilized for application and valuation of the image quality measures. A comparison with other image techniques is illustrated regarding the visual review.

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Keywords

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