Open Access

ARTICLE

A New Medical Image Enhancement Algorithm Based on Fractional Calculus

Hamid A. Jalab^1,*, Rabha W. Ibrahim², Ali M. Hasan³, Faten Khalid Karim⁴, Ala’a R. Al-Shamasneh¹, Dumitru Baleanu^5,6,7

1 Faculty of Computer Science and Information Technology, University Malaya, Kuala Lumpur, 50603, Malaysia
2 Nonlinear Dynamics Research Center (NDRC), Ajman University, Ajman, 346, UAE
3 College of Medicine, Al-Nahrain University, Baghdad, 10001, Iraq
4 Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 84428, Saudi Arabia
5 Department of Mathematics, Cankaya University, Balgat, 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: Hamid A. Jalab. Email:

(This article belongs to the Special Issue: Recent Advances in Fractional Calculus Applied to Complex Engineering Phenomena)

Computers, Materials & Continua 2021, 68(2), 1467-1483. https://doi.org/10.32604/cmc.2021.016047

Received 20 December 2020; Accepted 20 January 2021; Issue published 13 April 2021

Abstract

The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images. The captured images may present with low contrast and low visibility, which might influence the accuracy of the diagnosis process. To overcome this problem, this paper presents a new fractional integral entropy (FITE) that estimates the unforeseeable probabilities of image pixels, posing as the main contribution of the paper. The proposed model dynamically enhances the image based on the image contents. The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’ probability. Initially, the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image. Next, the contrast of the image is then adjusted to enhance the regions with low visibility. Finally, the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image. Tests were conducted on brain MRI, lungs CT, and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality. The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores. Overall, this model improves the details of brain MRI, lungs CT, and kidney MRI scans, and could therefore potentially help the medical staff during the diagnosis process.

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Citations