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http://dx.doi.org/10.17946/JRST.2021.44.5.473

Comparison of Based on Histogram Equalization Techniques by Using Normalization in Thoracic Computed Tomography  

Lee, Young-Jun (Department of Radiology, The Seoul National University Hospital of Korea)
Min, Jung-Whan (Department of Radiological Technology The Shingu University of Korea)
Publication Information
Journal of radiological science and technology / v.44, no.5, 2021 , pp. 473-480 More about this Journal
Abstract
This study was purpose to method that applies for improving the image quality in CT and X-ray scan, especially in the lung region. Also, we researched the parameters of the image before and after applying for Histogram Equalization (HE) such as mean, median values in the histogram. These techniques are mainly used for all type of medical images such as for Chest X-ray, Low-Dose Computed Tomography (CT). These are also used to intensify tiny anatomies like vessels, lung nodules, airways and pulmonary fissures. The proposed techniques consist of two main steps using the MATLAB software (R2021a). First, the technique should apply for the process of normalization for improving the basic image more correctly. In the next, the technique actively rearranges the intensity of the image contrast. Second, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method was used for enhancing small details, textures and local contrast of the image. As a result, this paper shows the modern and improved techniques of HE and some advantages of the technique on the traditional HE. Therefore, this paper concludes that various techniques related to the HE can be helpful for many processes, especially image pre-processing for Machine Learning (ML), Deep Learning (DL).
Keywords
Medical Image Processing; Histogram Equalization; Contrast Limited Histogram Adaptive Equalization; CT; Thoracic Scout;
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1 Gonzalez RG, Woods RE. Digital Image Processing. 3rd ed. Publishing House of Electronics Industry, Beijing; 2008:129-78.
2 Gupta S, Purkayastha SS, et al. Image Enhancement and Analysis of Microscopic Images using Various Image Processing Techniques. International Journal of Engineering Research and Applications(IJERA). 2012;2(3):44-8.
3 Lavania KK, Kumar R, et al. Image Enhancement Using Filtering Techniques. International Journal on Computer Science and Engineering(IJCSE). 2012;4(1):14-20.
4 Tang J, Peli E, Acton S. Image Enhancement Using a Contrast Measure in the Compressed Domain. IEEE Signal Processing Letters. 2003;10(10):289-92.   DOI
5 Pizer SM, Amburn EP, Austin JD, et al. Adaptive Histogram Equalization and Its Variations. Computer Vision, Graphics, and Image Processing. 1987;39: 355-68.   DOI
6 Zuiderveld K. Contrast Limited Adaptive Histogram Equalization. Academic Press; 1994.
7 Shanmugavadivu P, Balasubramanian K. Thresholded and Optimized Histogram Equalization for contrast enhancement of images q. Comput. Electr. Eng. 2014;40(3):757-68.   DOI
8 Kim YK, Kim YM. Comparison of Estimated and Measured Doses of Dual-energy Computed Tomography. Journal of Radiological Science and Technology. 2018;41(5):405-11.   DOI
9 Sonker D, Parsai MP. Comparison of Histogram Equalization Techniques for Image Enhancement of Grayscale images of Dawn and Dusk. International Journal of Modern Engineering Research(IJMER), 2013;3(4):2476-80.
10 Sengee N, Sengee A, Choi HK. Image contrast enhancement using bi-histogram equalization with neighborhood metrics. IEEE Trans. Consum. Electron. 2010;56(4):2727-34.   DOI
11 Park KI. Fundamentals of Probability and Stochastic Processes with Applications to Communications. Springer; 2018:77-84.
12 Singh K, Kapoor R. Image enhancement using Exposure based Sub Image Histogram Equalization. Pattern Recognit. Lett. 2014;36(1):10-4.   DOI
13 Vishwakarma VP, Pandey S, Gupta MN. Adaptive histogram equalization and logarithm transform with rescaled low frequency DCT coefficients for illumination normalization. Int. J. Recent Trends Eng. Technol. 2009;1(1):318-22.
14 Min JW, Jeong HW. Evaluation of Resolution Characteristics by Using Chart Device Angle. Journal of Radiological Science and Technology. 2021;44(4):375-80.   DOI
15 Devore JL, Berk KN. Modern Mathematical Statistics with Applications. Cengage; 2017:263.
16 Stirzaker D. Elementary Probability. Cambridge University Press; 2017.
17 Abubakar FM. Image Enhancement using Histogram Equalization and Spatial Filtering. International Journal of Science and Research(IJSR). 2012;1(3): 105-7.
18 Chen SD, Ramli AR. Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement. IEEE Transactions on Consumer Electronics. 2003; 49(4):1310-9.   DOI
19 Garg R, Mittal B, Garg S. Histogram Equalization Techniques For Image Enhancement. Int. J. Electron. Commun. Technol. 2011;2(1):107-11.
20 Tan TL, Sim KS, Tso CP. Image enhancement using background brightness preserving histogram equalization. Electron. Lett. 2012;48(3):155.   DOI