A New Medical Image Enhancement Algorithm Based on Fractional Calculus

作     者：Hamid A.Jalab Rabha W.Ibrahim Ali M.Hasan Faten Khalid Karim Ala’a R.Al-Shamasneh Dumitru Baleanu 

作者机构：Faculty of Computer Science and Information TechnologyUniversity MalayaKuala Lumpur50603Malaysia Nonlinear Dynamics Research Center(NDRC)Ajman UniversityAjman346UAE College of MedicineAl-Nahrain UniversityBaghdad10001Iraq Department of Computer ScienceCollege of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of MathematicsCankaya UniversityBalgatAnkara06530Turkey Institute of Space SciencesMagurele-BucharestR76900Romania Department of Medical ResearchChina Medical UniversityTaichung40402Taiwan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第8期

页      面：1467-1483页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：funded by the Deanship of Scientic Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Progr 

主　　题：Fractional calculus image enhancement brain MRI lungs CT kidney MRI 

摘      要：The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured *** captured images may present with low contrast and low visibility,which might inuence the accuracy of the diagnosis *** 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 *** proposed model dynamically enhances the image based on the image *** main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’***,the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the ***,the contrast of the image is then adjusted to enhance the regions with low ***,the fractional integral entropy approach is implemented to enhance the low visibility contents from the input *** 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 *** obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE ***,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.