Abstract
Analysis of molecular and medical images is an important area of interdisciplinary research. Accurate interpretation and understanding of those images is increasingly demanding because it opens doors to accurate diagnoses of diseases and novel biomedical discovery. During the image collection, imaging devices are quite often interfered by various noise sources. Impulse noise degrades biomedical image details such as edges, contours and texture. In this paper we present a robust technique for filtering impulse-noise degraded biomedical images. The proposed filter is based on noise detector and cubic interpolation. Experimental results on several types of biomedical images and comparisons with several existing noise-filtering models have demonstrated that not only the proposed filter is effective for noise removal but also for image detail preservation.
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References
Chiew, M., Graham, S.J.: Bold contrast and noise characteristics of densely sampled multi-echo fMRI Data. IEEE Transactions on Medical Imaging 30(9), 1691–1703 (2011)
Li, C.Q., Magland, J.F., Rajapakse, C.S.: Implications of resolution and noise for in vivo micro-MRI of trabecular bone. Medical Physics 35(12), 5584–5594 (2008)
Tapiovaara, M.J., Wagner, R.F.: SNR and noise measurements for medical imaging: a practical approach based on statistical decision theory. Medical Physics 38(1), 71–92 (1993)
Gravel, P., Beaudoin, G., De Guise, J.A.: A method for modeling noise in medical images. IEEE Transactions on Medical Imaging 23, 1221–1232 (2004)
Abd-Elmoniem, K.Z., Youssef, A.B., Kadah, Y.M.: Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion. IEEE Transactions on Biomedical Engineering 49(9), 997–1014 (2002)
Adam, D., Beilin-Nissan, S., Friedman, Z., Behar, V.: The combined effect of spatial compounding and nonlinear filtering on the speckle reduction in ultrasound images. Ultrasonics 44(2), 166–181 (2006)
Mondal, P.P., Rajan, K., Ahmad, I.: Filter for biomedical imaging and image processing. Journal of The Optical Society of America A-optics Image Science and Vision 23(7), 1678–1686 (2006)
Toprak, A., Gueler, I.: Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter. Digital Signal Processing 17(4), 711–723 (2007)
Toprak, A., Ozerdem, M.S., Guler, I.: Suppression of impulse noise in MR images using artificial intelligent based neuro-fuzzy adaptive median filter. Digital Signal Processing 18(3), 391–405 (2008)
Ko, S.J., Lee, Y.H.: Center weighted median filters and their applications to image-enhancement. IEEE Transactions on Circuits and Systems 38(9), 984–993 (1991)
Sun, T., Neuvo, Y.: Detail-preserving median based filters in image-processing. Pattern Recognition Letters 15(4), 341–347 (1994)
Raymond, H.C., Chung, W.H., Mila, N.: Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Transactions on Image Processing 14(10), 1479–1485 (2005)
Srinivasan, K.S., Ebenezer, D.: A new fast and efficient decision based algorithm for impulse noise removal. IEEE Signal Processing Letters 14(3), 189–192 (2007)
Schulte, S., De, W., Valerie, V.D., Nachtegael, M., Kerre, E.E.: A fuzzy impulse noise detection and reduction method. IEEE Transactions on Image Processing 15(5), 1153–1162 (2006)
Gerig, G., Kubler, O., Kikinis, R., Jolesz, F.A.: Nonlinear anisotropic filtering of MRI data. IEEE Transactions on Medical Imaging 11, 221–232 (1992)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)
Keys, R.G.: Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech and Signal Processing ASSP-29(6), 1153–1160 (1981)
Reichenbach, S.E., Geng, F.: Two-dimensional cubic convolution. IEEE Transactions on Image Processing 12(8), 857–865 (2003)
Shi, J.Z., Reichenbach, S.E.: Image interpolation by two-dimensional parametric cubic convolution. IEEE Transactions on Image Processing 15(7), 1857–1870 (2006)
Wu, J., Tang, C.: An efficient decision-based and edge-preserving method for salt-and-pepper noise removal. Pattern Recognition Letters 32(15), 1974–1981 (2011)
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Xu, J., Pham, T.D. (2012). Robust Impulse-Noise Filtering for Biomedical Images Using Numerical Interpolation. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_18
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DOI: https://doi.org/10.1007/978-3-642-31298-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31297-7
Online ISBN: 978-3-642-31298-4
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