Browse > Article
http://dx.doi.org/10.6109/jicce.2017.15.2.131

An Optimal Algorithm for Enhancing the Contrast of Chest Images Using the Frequency Filters Based on Fuzzy Logic  

Shin, Choong-Ho (Department of Computer Engineering, Chosun University)
Jung, Chai-Yeoung (Department of Computer Statistics, Chosun University)
Publication Information
Journal of information and communication convergence engineering / v.15, no.2, 2017 , pp. 131-136 More about this Journal
Abstract
Chest X-ray image cannot be focused in the same manner as optical lenses and the resultant image generally tends to be slightly blurred. Therefore, appropriate methods to improve the quality of chest X-ray image have been studied in this paper. As the frequency domain filters work well for slight blurring and moderate levels of additive noises, we propose an algorithm that is particularly suitable for enhancing chest image. First, the chest image using Gaussian high pass filter and the optimal high frequency emphasis filter shows improvements in the edges and contrast of the flat areas. Second, as compared to using histogram equalization where each pixel of chest image is characterized by a loss of detail and much noises, in using fuzzy logic, each pixel of chest image shows the detail preservation and little noise.
Keywords
Fuzzy logic; Gaussian high-pass filter; High-frequency emphasis filter;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 S. L. A. Lee, A. Z. Kouzani, and E. J. Hu, "Automated detection of lung nodules in computed tomography images: a review," Machine Vision and Applications, vol. 23, no. 1, pp. 151-163, 2012.   DOI
2 H. Jiang, Z. Wang, L. Ma, Y. Liu, and P. Li, "A novel method to improve the visual quality of X-ray CR Images," International Journal of Image, Graphics and Signal Processing, vol. 3, no. 4, pp. 25-31, 2011.   DOI
3 S. E. Umbaugh, Computer Vision and Image Processing: A Practical Approach Using CVIPtools. Upper Saddle River, NJ: Prentice Hall, 1998.
4 T. K. Kim and J. K. Paik, "Adaptive contrast enhancement using gain-controllable clipped histogram equalization," IEEE Transactions on Consumer Electronic, vol. 54, no. 4, pp. 1803- 1810, 2008.   DOI
5 I. F. Jafar, K. A. Darabkh and G. M. Al-Sukkar, "A rule-based fuzzy inference system for adaptive image contrast enhancement," The Computer Journal, vol. 55, no. 9, pp. 1041-1057, 2012.   DOI
6 A.K. Tripathi and S. Mukhopadhyay, "Efficient fog removal from video," Signal, Image and Video Processing, vol. 8, no. 8, pp. 1431-1439, 2014.   DOI
7 K. M. Kim, Y. S. Moon, J. J. Park, S. W. Jung, and G. T. Park, "The enhancement of medial image using edge-based histogram modification," Journal of the Institute of Electronics and Information Engineers B, vol. 32, no. 12, pp. 59-69, 1995.
8 R. H. Sherrier and G. A. Johnson, "Regionally adaptive histogram equalization of the chest," IEEE Transaction on Medical Imaging, vol. 6, no. 1, pp. 1-7, 1987.   DOI
9 C. H. Shin, C. Y. Jung, "An optimal method to improve the visual quality of medical images," Journal of Chosun Natural Science, vol. 8, no. 2, pp. 141-144, 2015.   DOI
10 C. H. Shin and C. Y. Jung, "An enhancement of medical image using optimized high-frequency filter," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 3, pp. 698-704, 2013.   DOI
11 M. I. Rajab, T. A. El-Benawy, and M. W. Al-Hazmi, "Application of frequency domain processing to X-ray radiographic images of welding defects," Journal of X-Ray Science and Technology, vol. 15, no. 3, pp. 147-156, 2007.
12 R. C. Gonzales, R. E. Woods, and S. L. Eddins, Digital Image Processing Using Matlab. Upper Saddle River, NJ: Prentice Hall, 2004.