Comparison of Based on Histogram Equalization Techniques by Using Normalization in Thoracic Computed Tomography
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
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).
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).
참고문헌 (20)
- Gonzalez RG, "Digital Image Processing", pp.129~178 (2008), Publishing House of Electronics Industry
- Gupta S, "Image Enhancement and Analysis of Microscopic Images using Various Image Processing Techniques", International Journal of Engineering Research and Applications(IJERA), 2(3), pp.44~48 (2012)
- Lavania KK, "Image Enhancement Using Filtering Techniques", International Journal on Computer Science and Engineering(IJCSE), 4(1), pp.14~20 (2012)
- Tang J, "Image Enhancement Using a Contrast Measure in the Compressed Domain", IEEE Signal Processing Letters, 10(10), pp.289~292 (2003)
- Pizer SM, "Adaptive Histogram Equalization and Its Variations", Computer Vision, Graphics, and Image Processing, 39, pp.355~368 (1987)
- Zuiderveld K., "Contrast Limited Adaptive Histogram Equalization" (1994), Academic Press
- Abubakar FM, "Image Enhancement using Histogram Equalization and Spatial Filtering", International Journal of Science and Research(IJSR), 1(3), pp.105~107 (2012)
- Sonker D, "Comparison of Histogram Equalization Techniques for Image Enhancement of Grayscale images of Dawn and Dusk", International Journal of Modern Engineering Research(IJMER), 3(4), pp.2476~2480 (2013)
- Devore JL, "Modern Mathematical Statistics with Applications", Cengage, pp.263~ (2017)
- Stirzaker D., "Elementary Probability" (2017), Cambridge University Press
- Park KI, "Fundamentals of Probability and Stochastic Processes with Applications to Communications", pp.77~84 (2018), Springer
- Chen SD, "Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement", IEEE Transactions on Consumer Electronics, 49(4), pp.1310~1319 (2003)
- Sengee N, "Image contrast enhancement using bi-histogram equalization with neighborhood metrics", IEEE Trans. Consum. Electron, 56(4), pp.2727~2734 (2010)
- Shanmugavadivu P, "Thresholded and Optimized Histogram Equalization for contrast enhancement of images q", Comput. Electr. Eng, 40(3), pp.757~768 (2014)
- Garg R, "Histogram Equalization Techniques For Image Enhancement", Int. J. Electron. Commun. Technol, 2(1), pp.107~111 (2011)
- Singh K, "Image enhancement using Exposure based Sub Image Histogram Equalization", Pattern Recognit. Lett, 36(1), pp.10~14 (2014)
- Vishwakarma VP, "Adaptive histogram equalization and logarithm transform with rescaled low frequency DCT coefficients for illumination normalization", Int. J. Recent Trends Eng. Technol, 1(1), pp.318~322 (2009)
- Tan TL, "Image enhancement using background brightness preserving histogram equalization", Electron. Lett, 48(3), pp.155~ (2012)
- 김영균, "Dual-energy 컴퓨터단층촬영에서 장비 제공선량과 측정선량 비교", 방사선기술과학, 41(5), pp.405~411 (2018), 대한방사선과학회[DOI]
- 민정환, "차트 각도를 이용한 해상력 특성 평가", 방사선기술과학, 44(4), pp.375~380 (2021), 대한방사선과학회