Biomedical Image AnalysisNo Access

Medical X-Ray Image Enhancement Using Global Contrast-Limited Adaptive Histogram Equalization

Sohrab Namazi Nia

Department of Computer Science, New Jersey, Institute of Technology Newark, NJ 07102, USA

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and

Frank Y. Shih

https://orcid.org/0000-0002-1507-0285

Department of Computer Science, New Jersey, Institute of Technology Newark, NJ 07102, USA

Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan

E-mail Address: shih@njit.edu

Corresponding author.

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https://doi.org/10.1142/S0218001424570106Cited by:0 (Source: Crossref)

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.

Keywords:

References

1. A. Bakar, K. Muchtar, F. Arnia, T. Y. Arif, H. Kurniawan and N. Maulina , Image enhancement effects on chest X-ray images for COVID-19 detection, in 2023 3rd Int. Conf. Computing and Information Technology, 13–14 September 2003, Tabuk, Saudi Arabia, pp. 243–248. Google Scholar
2. C. H. Bindu and B. S. Chandra , Medical images enhancement by homomorphic filtering equalization, Int. J. Image Process. Appl. 3(7) (2016) 183–185. Google Scholar
3. H. Ibrahim and N. S. P. Kong , Brightness preserving dynamic histogram equalization for image contrast enhancement, IEEE Trans. Consum. Electron. 53(4) (2007) 1752–I758. Crossref, Web of Science, Google Scholar
4. I. Belykh , Homomorphic filtering for radiographic image contrast enhancement and artifacts elimination, in Proc. 3rd Int. Conf. on Frontiers of Intelligent Computing: Theory and Applications (Springer, Cham, 2014), pp. 423–430. Google Scholar
5. D. Kermany, K. Zhang and M. Goldbaum, Large dataset of labeled optical coherence tomography (OCT) and chest X-Ray images, Mendeley Data, Version 3 (2018). Google Scholar
6. M. Kim and M. G. Chung , Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement, IEEE Trans. Consum. Electron. 54(3) (2008) 1389–1397. Crossref, Web of Science, Google Scholar
7. T. Kim and J. Paik , Adaptive contrast enhancement using gain-controllable clipped histogram equalization, IEEE Trans. Consum. Electron. 54(4) (2008) 1803–1810. Crossref, Web of Science, Google Scholar
8. K. K. Lavania and R. Kumar , Image enhancement using filtering techniques, Int. J. Comput. Sci. Eng. 4(1) (2012) 14–20. Google Scholar
9. S. M. Pizer et al., Adaptive histogram equalization and its variations, Comput. Vis. Graph. Image Process. 39(3) (1987) 355–368. Crossref, Google Scholar
10. S. M. Pizer, R. E. Johnston, J. P. Ericksen, B. C. Yankaskas and K. E. Muller , Contrast-limited adaptive histogram equalization: Speed and effectiveness, in Proc. First Conf. on Visualization in Biomedical Computing, 22–25 May 1990, Atlanta, GA, pp. 337–345s. Google Scholar
11. A. W. Setiawan , Effect of chest X-Ray contrast image enhancement on pneumonia detection using convolutional neural networks, in 2021 IEEE Int. Biomedical Instrumentation and Technology Conf., 20–21 October 2021, Yogyakarta, Indonesia, pp. 142–147. Google Scholar
12. S. Setty, N. K. Srinath and M. C. Hanumantharaju , Development of multiscale retinex algorithm for medical image enhancement based on multi-rate sampling, in Proc. Int. Conf. on Signal Processing, Image Processing & Pattern Recognition, 7–8 February 2013, Coimbatore, India, pp. 145–150. Google Scholar
13. M. Sharma and D. Kumar , Comparative analysis of image enhancement techniques for chest X-ray images, in 2022 Int. Conf. on Computational Intelligence and Sustainable Engineering Solutions, 20–21 May 2022, Greater Noida, India, pp. 130–135. Google Scholar
14. M. H. Siddiqi , X-Ray image enhancement using global histogram equalization, in Proc. 29th Int. Conf. on Computer Theory and Applications, 29–31 October 2019, Alexandria, Egypt, pp. 90–95. Google Scholar
15. S. Sukhjinder, R. K. Bansal and S. Bansal , Medical image enhancement using histogram processing techniques followed by median filter, Int. J. Image Process. Appl. 3(1) (2012) 1–9. Google Scholar
16. R. Wang and G. Wang , Medical X-ray image enhancement method based on TV-homomorphic filter, in Proc. 2nd Int. Conf. on Image, Vision and Computing, 2–4 June 2017, Chengdu, China, pp. 315–318. Google Scholar
17. G. Yadav, S. Maheshwari and A. Agarwal , Contrast limited adaptive histogram equalization based enhancement for real time video system, in Proc. Int. Conf. on Advances in Computing, Communications and Informatics, 24–27 September 2014, Delhi, India, pp. 2392–2397. Google Scholar
18. Z. Zhao and Y. Zhou , PLIP based unsharp masking for medical image enhancement, in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 20–25 March 2016, Shanghai, China, pp. 1238–1242. Google Scholar
19. K. Zuiderveld , Contrast limited adaptive histogram equalization, in Graphics Gems lV, ed. P. Heckbert (Academic Press, 1994). Crossref, Google Scholar