Advanced SearchSearch Tips
An Enhanced Algorithm for an Optimal High-Frequency Emphasis Filter Based on Fuzzy Logic for Chest X-Ray Images
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
An Enhanced Algorithm for an Optimal High-Frequency Emphasis Filter Based on Fuzzy Logic for Chest X-Ray Images
Shin, Choong-Ho; Lee, Jung-Jai; Jung, Chai-Yeoung;
  PDF(new window)
The chest X-ray image cannot be focused in the same manner that optical lenses are and the resultant image generally tends to be slightly blurred. Therefore, the methods to improve the quality of chest X-ray image have been studied. In this paper, the inherent noises of the input images are suppressed by adding the Laplacian image to the original. First, the chest X-ray image using an Gaussian high pass filter and an optimal high frequency emphasis filter has shown improvements in the edges and contrast of flat areas. Second, using fuzzy logic_histogram equalization, each pixel of the chest X-ray image shows the normal distribution of intensities that are not overexposed. As a result, the proposed method has shown the enhanced edge and contrast of the images with the noise canceling effect.
Fuzzy logic_histogram equalization;Gaussian high-pass filter;High-frequency emphasis filter;Laplacian image;
 Cited by
J. H. Austin, N. L. Muller, P. J. Friedman, D. M. Hansell, D. P. Naidich, M. Remy-Jardin, W. R. Webb, and E. A. Zerhouni, “Glossary of terms for CT of the lungs: recommendations of the Nomenclature Committee of the Fleischner Society,” Radiology, vol. 200, no. 2, pp. 327-331, 1996. crossref(new window)

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. crossref(new window)

T. Ji, M. K. Sundareshan, and H. Roehrig, "New algorithm for adaptive contrast enhancement based on human visual properties for medical imaging applications," in Medical Imaging 1994 (Proceedings of the SPIE vol. 2167). Bellingham, WA: SPIE, pp. 561-572, 1994.

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.

C. H. Shin and C. Y. Jung, “An enhancement of medical image using optimized high-frequency emphasis filter, Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 3, pp. 698-704, 2013. crossref(new window)

R. C. Gonzales, R. E. Woods, and S. L. Eddins, Digital Image Processing Using Matlab. Upper Saddle River, NJ: Prentice Hall, 2004.

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 (IJIGSP), vol. 3, no. 4, pp. 25-31, 2011. crossref(new window)

S. E. Umbaugh, Computer Vision and Image Processing: A Practical Approach Using CVIPtools. Upper Saddle River, NJ: Prentice Hall, 1998.