Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

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1.60

CiteScore

Ishtiaq Rasool Khan1This email address is being protected from spambots. You need JavaScript enabled to view it., Waqar Mirza2, Asif Siddiq2, Seong-O Shim1

1University of Jeddah, College of Computer Science and Engineering, Jeddah, Saudi Arabia, 21589

2Pakistan Institute Of Engineering And Technology, Department of Electrical Engineering, Multan, Pakistan, 61000


 

Received: March 7, 2023
Accepted: June 18, 2023
Publication Date: July 15, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202403_27(3).0001  


Medical imaging enables doctors to provide faster and more accurate diagnoses of different conditions. Medical images are generally very dark and frequently exhibit degradations such as poor detail or low contrast, which may impact the accuracy and speed of diagnosis. This paper proposes an effective and efficient technique to enhance CT images. The image is first decomposed into base and detail layers, which are individually enhanced using adaptive gamma correction, and recombined to obtain the resultant image with better details and brightness without added noise and artifacts such as halo. We present a comprehensive study using 51 test images evaluated by 50 human subjects to compare the performance of the proposed method with the existing state of the art. In addition, we use six commonly used objective metrics to score the images produced by the proposed method and seven existing state of the art enhancement methods. The proposed method outperforms the existing techniques in both objective and subjective evaluations and appears as the most effective way of enhancing medical images’ quality without producing artifacts.


Keywords: Medical imaging; CT Images; image enhancement; gamma correction; image decomposition


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