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x-ray-image-enhancement-using-g-clahe

Novel Method for Image Enhancement, specifically, Medical X-ray images

https://github.com/sohrabnamazinia/x-ray-image-enhancement-using-g-clahe

Science Score: 44.0%

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Repository

Novel Method for Image Enhancement, specifically, Medical X-ray images

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  • Host: GitHub
  • Owner: sohrabnamazinia
  • Default Branch: main
  • Size: 7.81 KB
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  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Created almost 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme Citation

README.md

X-ray-Image-Enhancement-using-G-CLAHE

Novel Method for Image Enhancement, specifically, Medical X-ray images

This repository contains the source code for the project based on our research paper. You can find the paper here:

G-CLAHE: Link to the Paper

If you find this work useful or incorporate it into your own research or projects, please kindly cite our paper.

To cite this repository, you can use the citation option on the right-hand side ("Cite this repository"), or use the provided citation.bib file for BibTeX format.

Thank you for respecting our work!

The project source code will be added soon. If you need to access the source code now, you can reach out to me via email.

Owner

  • Name: Sohrab Namazi
  • Login: sohrabnamazinia
  • Kind: user
  • Location: NJ, USA

Computer Science student | 22 years old

Citation (CITATION.bib) 






@article{doi:10.1142/S0218001424570106,
author = {Nia, Sohrab Namazi and Shih, Frank Y.},
title = {Medical X-Ray Image Enhancement Using Global Contrast-Limited Adaptive Histogram Equalization},
journal = {International Journal of Pattern Recognition and Artificial Intelligence},
volume = {0},
number = {0},
pages = {2457010},
year = {0},
doi = {10.1142/S0218001424570106},

URL = { 
    
        https://doi.org/10.1142/S0218001424570106
    
    

},
eprint = { 
    
        https://doi.org/10.1142/S0218001424570106
    
    

}
,
    abstract = { In medical imaging, accurate diagnosis heavily relies on effective image enhancement techniques, particularly for X-ray images. Existing methods often suffer from various challenges such as sacrificing global image characteristics over local image characteristics or vice versa. In this paper, we present a novel approach, called G-CLAHE (Global-Contrast Limited Adaptive Histogram Equalization), which perfectly suits medical imaging with a focus on X-rays. This method adapts from Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to take both advantages and avoid weakness to preserve local and global characteristics. Experimental results show that it can significantly improve current state-of-the-art algorithms to effectively address their limitations and enhance the contrast and quality of X-ray images for diagnostic accuracy. }
}

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