Implementation of Grayscale Image Transformation and Histogram Equalization Methods in Digital Image Processing
DOI:
https://doi.org/10.58982/krisnadana.v4i2.739Keywords:
Digital Image, Grayscale Image, Histogram EqualizationAbstract
Medical digital image processing is typically color-based and exhibits varying quality, which necessitates further processing to enhance visibility and contrast. The main issue addressed in this study is how to process color images to produce higher-quality images that facilitate analysis and highlight hidden features. This research proposes several image processing methods to tackle these problems, including color image (RGB) to grayscale conversion, image inversion, constant addition, and histogram equalization. Each step aims to improve the contrast and quality of medical images, producing clearer and more informative results. Histograms are used to analyze pixel intensity distribution, while histogram equalization serves to normalize the intensity distribution and enhance image contrast. The results of this study demonstrate that by applying these techniques, medical images can experience significant quality improvement, with sharper contrast and more evenly distributed pixel intensities. This process allows previously hidden areas in dark or bright regions of the image to become clearer. The processed images help in further analysis and can improve the accuracy of medical diagnosis.
References
R. Archana and P. S. E. Jeevaraj, Deep learning models for digital image processing: a review, vol. 57, no. 1. Springer Netherlands, 2024. doi: 10.1007/s10462-023-10631-z.
R. C. Gonzalez, Digital Image Processing. New York: Library of Congress Cataloging in Publication data on File, 2008. [Online]. Available: www.EBooksWorld.ir
M. Sonka, V. Hiavac, and R. Boyle, Image processing, Analysis, and Machine Vision. USA: Global Engineering, 2015. [Online]. Available: www.cengage.com/highered
Y. T. Kim, "Contrast enhancement using brightness preserving bi-histogram equalization," IEEE Trans. Consum. Electron., vol. 43, no. 1, pp. 1-8, 1997, doi: 10.1109/30.580378.
A. M. Reza, "Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement," J. VLSI Signal Process. Syst. Signal Image. Video Technol., vol. 38, no. 1, pp. 35-44, 2004, doi: 10.1023/B:VLSI.0000028532.53893.82.
S. M. Pizer et al., "Adaptive histogram equalization and its variations," Computer Vision, Graphics, and Image Processing, vol. 38, no. 1. p. 99, 1987. doi: 10.1016/s0734-189x(87)80156-1.
Y. Chang, C. Jung, P. Ke, H. Song, and J. Hwang, "Automatic Contrast-Limited Adaptive Histogram Equalization with Dual Gamma Correction," IEEE Access, vol. 6, pp. 11782-11792, 2018, doi: 10.1109/ACCESS.2018.2797872.
J. Caron and J. R. Markusen, The Image Processing Handbook. CRC Press Taylor & Francis Group, 2016. [Online]. Available: https://doi.org/10.1201/b18983
C. Yang, M. Jin, Y. Xu, R. Zhang, Y. Chen, and H. Liu, "SepLUT: Separable Image-Adaptive Lookup Tables for Real-Time Image Enhancement," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13678 LNCS, pp. 201-217, 2022, doi: 10.1007/978-3-031-19797-0_12.
M. K. Moghimi and F. Mohanna, "Real-time underwater image enhancement: a systematic review," J. Real-Time Image Process., vol. 18, no. 5, pp. 1509-1525, 2021, doi: 10.1007/s11554-020-01052-0.
V. Tyagi, "Understanding Digital Image Processing," Underst. Digit. Image Process., no. November, 2018, doi: 10.1201/9781315123905.
C. Suresh, S. Singh, R. Saini, and A. K. Saini, "A Comparative Analysis of Image Scaling Algorithms," Int. J. Image, Graph. Signal Process, vol. 5, no. 5, pp. 55-62, 2013, doi: 10.5815/ijigsp.2013.05.07.
S. Jamil, M. J. Piran, M. U. Rahman, and O. J. Kwon, "Learning-driven lossy image compression: A comprehensive survey," Eng. Appl. Artif. Intell., vol. 123, pp. 1-14, 2023, doi: 10.1016/j.engappai.2023.106361.
F. Hou et al., "Poisson Vector Graphics (PVG)," IEEE Trans. Vis. Comput. Graph., vol. 26, no. 2, pp. 1361-1371, 2020, doi: 10.1109/TVCG.2018.2867478.
J. Cal-Gonzalez et al., "Hybrid imaging: Instrumentation and data processing," Front. Phys., vol. 6, no. MAY, 2018, doi: 10.3389/fphy.2018.00047.
B. Sitohang and A. Sindar, "Analysis and Comparison of Sobel Edge Detection and Prewit Methods on Edge Detection of Sri Lanka Leaf Image," J. Nas. Computing and Technol. Inf., vol. 3, no. 3, pp. 314-322, 2020, doi: 10.32672/jnkti.v3i3.2511.
L. P. Varoslavskiy, Digital Image Processing., vol. 31-32, no. 11. 1977.
Savakar G. Dayanand and S. Pujar, "Digital Image Watermarking Using DWT and FWHT," Int. J. Image, Graph. Signal Process, vol. 10, no. 6, pp. 50-67, 2018, doi: 10.5815/ijigsp.2018.06.06.
D. Riana, Kusnadi, and M. Syahrani, "Digital Image Management Using Gryascale Transformation and Histogram Equalization Methods," J. Tech. Inform. Kaputama, vol. 6, no. 1, pp. 108-119, 2022, [Online]. Available: https://jurnal.kaputama.ac.id/index.php/JTIK/article/view/724
Y. N. Nabusa, "Digital Image Processing Comparison of Histogram Equalization and Specification Methods on Grey Images," J-Icon, vol. 7, no. 1, pp. 87-95, 2019.
S. Yulina, "Application of Haar Cascade Classifier in Face Detection and Grayscale Image Transformation Using OpenCV," J. Comput. Applied, vol. 7, no. 1, pp. 100-109, 2021, [Online]. Available: https://jurnal.pcr.ac.id/index.php/jkt/
A. Kaur and G. Kaur, "A review on image enhancement with deep learning approach," Accent. Trans. Image Process. Comput. Vis., vol. 4, no. 11, pp. 16-20, 2018, doi: 10.19101/tipcv.2018.411002.
S. Roy, K. Bhalla, and R. Patel, "Mathematical analysis of histogram equalization techniques for medical image enhancement: a tutorial from the perspective of data loss," Multimed. Tools Appl., vol. 83, no. 5, pp. 14363-14392, 2024, doi: 10.1007/s11042-023-15799-8.
H. Ibrahim et al., "Efficient color image enhancement using piecewise linear transformation and gamma correction," J. Opt., vol. 53, no. 3, pp. 2027-2037, 2024, doi: 10.1007/s12596-023-01171-4.
E. Baidoo, "Implementation of Gray Level Image Transformation Techniques," Int. J. Mod. Educ. Comput. Sci., vol. 10, no. 5, pp. 44-53, 2018, doi: 10.5815/ijmecs.2018.05.06.
T. Trongtirakul and S. S. Agaian, "Weighted Histogram Equalization Using Entropy of Probability Density Function," vol. 1, pp. 461-464, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Kusnadi Kusnadi, Diyaa Aaisyah Salmaa Putri Atmaja
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
