Academic Journal of Computing & Information Science, 2022, 5(6); doi: 10.25236/AJCIS.2022.050608.
Xuan Ren1, Songning Lai2
1School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu, Shanghai, 200093, China
2School of Information Science and Engineering, Shangdong University, No.72 Binhai Road, Jimo District, Qingdao, Shandong, 266000, China
Medical image enhancement is one of the most widely used medical image processing techniques in medical domain. Its purpose is to improve the visual effect of the image and facilitate the analysis and understanding of the image by human or machine. The Laplace transform and the Sobel gradient operator are two common ways of performing edge detection, image sharpening and enabling the image to be enhanced. However, each has limitations when used in isolation. The Laplace operator has a good edge detection effect, but it will make the image noise expand; the Sobel operator has a certain ability to smooth the noise, but the edges of the image obtained after processing are rougher. This paper therefore proposes a method based on both Laplace transform and Sobel operator, and histogram equalization of the transformed image is processed to enhance the image. Using a combination of both filtering methods avoids the disadvantage. This method was found to be effective in improving the quality of lung images and skeletal images through several experiments.
Medical image enhancement, Laplace transformation, Sobel operator, Histogram equalization
Xuan Ren, Songning Lai. Medical Image Enhancement Based on Laplace Transform, Sobel Operator and Histogram Equalization. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 6: 48-54. https://doi.org/10.25236/AJCIS.2022.050608.
[1] ChunYu Ning. Research of Post-processing for Medical Image and the Application in X-ray Image Optimization[D]. Jilin University,2005.
[2] GUO Yongkun, ZHU Yanchenl, LIU Liping, HUANG Qiang. Research Review of space-frequency domain image enhancement methods[J/OL]. Computer Engineering and Applications:1-16[2022-03-12].
[3] Yaonan Wang, Shutao Li, Jianxu Mao. Computer Image Processing and Recognition Technology[M]. Beijing: Higher Education Press, 2001.
[4] Yannan Zhou. The Research and Application of Medical Image Processing and Image Quality Assessment [D]. Lanzhou University of Technology. 2013.
[5] Liping Zhang, Lianqing Huang. Medical Image Enhancing Method Based on Locally Redistributed Histograms [J]. Journal of Optoelectronics,2004(07):877-880.
[6] Yan Chen, Guohua Geng. AN APPLICATION OF IMAGE ENHANCEMENT TO CLASSIFICATION OF MEDICAL IMAGES [J]. Computer Applications and Software, 2007(06):26-27+32.
[7] Peiyu Yan. Research and implement of lung CT image enhancement algorithm[D]. Shenyang Aerospace University, 2009.
[8] Jun Wu, Jianhua Yang, Chao He, Yuanzhong Zhu, Xiaowen Chen. Application of image enhancement technology in digital X-ray medical image[J]. China Medical Equipment, 2012,9(05):60-62.
[9] Li Menghang, Gao Shanshan, Han Huijian and Zhang Caiming.L0 Optimization Using Laplacian Operator for Image Smoothing[J]. Journal of Computer-Aided Design & Computer Graphics, 2021,33(07):1000-1014.
[10] TAN Yongqian, ZENG Fanju. Neural style transfer algorithm based on Laplacian operator and color preservation [J]. Journal of Computer Applications, 2019, 21(4):1-15.
[11] LI Zhonghai, JIN Haiyang, XING Xiaohong. Edge detection algorithm of fractional order Sobel operator for integer order differential filtering[J]. Computer Engineering and Applications, 2018, 54(4): 179-184.
[12] C. Deng, W. Ma and Y. Yin, An edge detection approach of image fusion based on improved Sobel operator, 2011 4th International Congress on Image and Signal Processing, 2011, pp. 1189-1193, doi: 10.1109/CISP.2011.6100499.
[13] Tawsifur Rahman, Amith Khandakar, Yazan Qiblawey, Anas Tahir, Serkan Kiranyaz, Saad Bin Abul Kashem, Mohammad Tariqul Islam, Somaya Al Maadeed, Susu M. Zughaier, Muhammad Salman Khan, Muhammad E.H. Chowdhury, Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images, Computers in Biology and Medicine, Volume 132,2021,104319,ISSN 0010-4825,
[14] M. C. Catalbas, D. Issever and A. Gulten, Morphological feature extraction with local histogram equalization,2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015, pp. 435-438, doi: 10.1109/SIU.2015.7129852.
[15] W. Okado, T. Goto, S. Hirano and M. Sakurai, Fast and high-quality regional histogram equalization,2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE), 2013, pp. 445-446, doi: 10.1109/GCCE.2013.6664884.