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      Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters

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          Abstract

          This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage. The second step is removing impulse noise with a median filter. The wavelet network presented here is a fixed one without learning. Experimental results show that our method acts on impulse noise effectively, and at the same time preserves chromaticity and image details very well.

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          Using wavelet network in nonparametric estimation

          Qinghua Zhang (1997)
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            A new efficient approach for the removal of impulse noise from highly corrupted images.

            E. Abreu, M Lightstone, S.K. Mitra … (1996)
            A new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable defined as the output of a classifier that operates on the differences between the input pixel and the remaining rank-ordered pixels in a sliding window. As part of this framework, several algorithms are examined, each of which is applicable to fixed and random-valued impulse noise models. First, a simple two-state approach is described in which the algorithm switches between the output of an identity filter and a rank-ordered mean (ROM) filter. The technique achieves an excellent tradeoff between noise suppression and detail preservation with little increase in computational complexity over the simple median filter. For a small additional cost in memory, this simple strategy is easily generalized into a multistate approach using weighted combinations of the identity and ROM filter in which the weighting coefficients can be optimized using image training data. Extensive simulations indicate that these methods perform significantly better in terms of noise suppression and detail preservation than a number of existing nonlinear techniques with as much as 40% impulse noise corruption. Moreover, the method can effectively restore images corrupted with Gaussian noise and mixed Gaussian and impulse noise. Finally, the method is shown to be extremely robust with respect to the training data and the percentage of impulse noise.
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              A method for modeling noise in medical images.

              Gilles Beaudoin, Simon-Pierre Gravel, Angelo M. De Marzo (2004)
              We have developed a method to study the statistical properties of the noise found in various medical images. The method is specifically designed for types of noise with uncorrelated fluctuations. Such signal fluctuations generally originate in the physical processes of imaging rather than in the tissue textures. Various types of noise (e.g., photon, electronics, and quantization) often contribute to degrade medical images; the overall noise is generally assumed to be additive with a zero-mean, constant-variance Gaussian distribution. However, statistical analysis suggests that the noise variance could be better modeled by a nonlinear function of the image intensity depending on external parameters related to the image acquisition protocol. We present a method to extract the relationship between an image intensity and the noise variance and to evaluate the corresponding parameters. The method was applied successfully to magnetic resonance images with different acquisition sequences and to several types of X-ray images.
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Med Signals Sens
                Journal ID (publisher-id): JMSS
                Title: Journal of Medical Signals and Sensors
                Publisher: Medknow Publications & Media Pvt Ltd (India )
                ISSN (Print): 2228-7477
                ISSN (Electronic): 2228-7477
                Publication date (Print): Jan-Apr 2012
                Volume: 2
                Issue: 1
                Pages: 25-37
                Affiliations
                [1] Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
                [1 ] Department of Electrical and Engineering, Isfahan University of Technology, Isfahan, Iran
                [2 ] Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
                Author notes
                Address for correspondence: Mr. Amir Reza Sadri, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran. E-mail: ar_sadry@ 123456yahoo.com
                Article
                Publisher ID: JMSS-2-25
                PMC ID: 3592502
                PubMed ID: 23493998
                SO-VID: d3ffbf3f-8cb3-43be-a4a4-7d80754ce374
                Copyright © Copyright: © Journal of Medical Signals and Sensors
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 05 January 2012
                Date accepted : 17 January 2012
                Categories
                Subject: Original Article

                ScienceOpen disciplines: Radiology & Imaging
                Keywords: gray-level difference,impulse noise,wavelet network,average background difference
                Data availability:
                ScienceOpen disciplines: Radiology & Imaging
                Keywords: gray-level difference, impulse noise, wavelet network, average background difference

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