Noise Removal and Contrast Enhancement for X-Ray Images
DOI:
https://doi.org/10.14738/jbemi.31.1893Keywords:
Medical Image Processing, Noise Reduction, Contrast Enhancement, X-ray imagesAbstract
X-ray image plays a very important role in the medical diagnosis. To help the doctors for diagnosis of the disease, some algorithms for enhancing X-ray images were proposed in the past decades. However, the enhancement of images will also amplify the noise or produce distortion of image, which are unfavorable to the diagnosis. Therefore, appropriate techniques for noise suppression and contrast enhancement are necessary. This paper proposed an algorithm including two-stage filtering and contrast enhancement for X-ray images. By using adaptive median filter and bilateral filter, our method is able to suppress the mixed noise which contains Gaussian noise and impulsive noise, while preserving the important structures (e.g., edges) in the images. Afterwards, the contrast of image is enhanced by using gray-level morphology and contrast limited histogram equalization (CLAHE). In the experiments, we evaluate the performance of noise removal and contrast enhancement separately with quantitative indexes and visual results. For the mixed noise case, our method is able to achieve averaged PSNR 39.89 dB and averaged SSIM 0.9449; for the contrast enhancement, our method is able to enhance more detail structures (e.g., edges, textures) than CLAHE.References
(1) Langland, O.E., et al., Principles of Dental Imaging. Lippincott Williams & Wilkins, 2002.
(2) Ito, K. and Xiong, K., Gaussian filters for nonlinear filtering problems. Automatic Control, IEEE Transactions on, 2000. 45(5): p. 910-927.
(3) Kurt, B., et al., Medical images enhancement by using anisotropic filter and clahe. In Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium On, 2012. p. 1-4.
(4) Perona, P. and Malik, J., Scale-space and edge detection using anisotropic diffusion. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1990. 12(7): p. 629-639.
(5) Yu, Y. and Acton, S.T., Speckle reducing anisotropic diffusion. Image Processing, IEEE Transactions on , 2002. 11(11): p. 1260-1270.
(6) Paris, S., et al., A gentle introduction to bilateral filtering and its
applications. In ACM SIGGRAPH 2007 Courses, 2007.
(7) Tomasi, C. and Manduchi, R., Bilateral filtering for gray and color images. In Computer Vision, Sixth International Conference On, 1998. p. 839-846.
(8) Lin, H.-M. and Willson Jr, A.N., Median filters with adaptive length. Circuits and Systems, IEEE Transactions on, 1988. 35(6): p. 675-690.
(9) Ko, S.-J. and Lee, Y.H., Center weighted median filters and their
applications to image enhancement. Circuits and Systems, IEEE Transactions on, 1991. 38(9): p. 984-993.
(10) Tsai, C.-Y., An adaptive rank-ordered median image filter for removing salt-and-pepper noise. Master’s thesis, National Cheng-Kung University, 2006.
(11) Hwang, H., et al., Adaptive median filters: new algorithms and results. Image Processing, IEEE Transactions on, 1995. 4(4): p. 499-502.
(12) Lehr, J. and Capek, P., Histogram equalization of ct images. Radiology, 1985. 154(1): p. 163-169.
(13) Khalid, N.E.A., et al., Cr images of metacarpel cortical edge detection-bone profile histogram approximation method. In Intelligent and Advanced Systems (ICIAS), International Conference On, 2007. p. 702-708.
(14) Wang, Y., et al., Image enhancement based on equal area dualistic sub-image histogram equalization method. Consumer Electronics, IEEE Transactions on, 1999. 45(1): p. 68-75.
(15) Pizer, S.M., et al., Adaptive histogram equalization and its variations. Computer vision, graphics, and image processing, 1987. 39(3): p. 355-368.
(16) Stark, J.A., Adaptive image contrast enhancement using generalizations of histogram equalization. Image Processing, IEEE Transactions on, 2000. 9(5): p. 889-896.
(17) Sund, T. and Møystad, A., Sliding window adaptive histogram equalization of intraoral radiographs: effect on image quality. Dentomaxillofacial Radiology, 2014.
(18) Kim, Y.-T., Contrast enhancement using brightness preserving bi-histogram equalization. Consumer Electronics, IEEE Transactions on, 1997. 43(1): p. 1-8.
(19) Shoaib, M., et al., Design and implementation of efficient information retrieval algorithm for chest x-ray images. Journal of American Science, 5(4): p. 43-48.
(20) Sund, T. and Eilertsen, K., An algorithm for fast adaptive image binarization with applications in radiotherapy imaging. Medical Imaging, IEEE Transactions on, 2003. 22(1): p. 22-28.
(21) Noor, N.M., et al., Fish bone impaction using adaptive histogram equalization (ahe). In Computer Research and Development, 2010 Second International Conference On, 2010. p. 163-167.
(22) Ibrahim, H. and Kong, N.S.P., Brightness preserving dynamic histogram equalization for image contrast enhancement. Consumer Electronics, IEEE Transactions on, 2007. 53(4): p. 1752-1758.
(23) Yang, Y., et al., Medical image enhancement algorithm based on wavelet transform. Electronics letters , 2010. 46(2): p. 120-121.
(24) Shrimali, V., et al., Comparing the performance of ultrasonic liver image enhancement techniques: a preference study. IETE Journal of Research, 2010. 56(1): p. 4-10.
(25) Gonzalez, R.C. and Woods, R.E., Digital Image Processing (3rd Edition). Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2006.
(26) Mahmoud, T.A. and Marshall, S., Medical image enhancement using threshold decomposition driven adaptive morphological filter. In 16th European Signal Processing Conference (EUSIPCO), 2008.
(27) Agaian, S.S., et al., Transform-based image enhancement algorithms with performance measure. Image Processing, IEEE Transactions on, 2001. 10(3): p. 367-382.
