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Volume : III, Issue : IV, May - 2013 AN EFFICIENT MEDICAL IMAGE DENOISING ALGORITHM SRINIVASAKIRAN GOTTAPU AND M.VENUGOPAL RAO Published By : Laxmi Book Publication Abstract : Noise suppression in medical images is a particularly delicate and difficult task. A trade-off
between noise reduction and the preservation of actual image features has to be made in a way that
enhances the diagnostically relevant image content. Image processing specialists usually lack the
biomedical expertise to judge the diagnostic relevance of the De-noising results. For example, in
ultrasound images, speckle noise may contain information useful to medical experts the use of speckled
texture for a diagnosis was discussed in. Also biomedical images show extreme variability and it is
necessary to operate on a case by case basis. This motivates the construction of robust and Efficient
denoising methods that are applicable to various circumstances, rather than being optimal under very
specific conditions. In this paper, we propose one robust method that adapts itself to various types of image
noise as well as to the preference of the medical expert: a single parameter can be used to balance the
preservation of relevant details against the degree of noise reduction. The proposed algorithm is simple to
implement and fast. We demonstrate its usefulness for denoising and enhancement of the CT, Ultrasound
and Magnetic Resonance images. Keywords : Article : Cite This Article : SRINIVASAKIRAN GOTTAPU AND M.VENUGOPAL RAO, (2013). AN EFFICIENT MEDICAL IMAGE DENOISING ALGORITHM. Indian Streams Research Journal, Vol. III, Issue. IV, http://oldisrj.lbp.world/UploadedData/2404.pdf References : - A. Piˇzurica, W. Philips, I. Lemahieu, and M. Acheroy, “DespecklingSAR images using wavelets and a new class of adaptiveshrinkage functions,” Proc. IEEE Internat. Conf. on Image Proc., ICIP 2001, Thessaloniki, Grece, Oct 2001.
- Achim A., Bezerianos A., and Tsakalides P., “Novel Bayesian multiscale method for speckle removal in medical ultrasound images,” IEEE Trans. on Medical Imaging, vol. 20, no. 8, pp. 772-783, Aug 2001.
- A. Piˇzurica, W. Philips, I. Lemahieu, and M. Acheroy, “A joint inter- and intrascale statistical model for Bayesian wavelet based imagedenoising,”IEEE Trans. Image Proc., vol. 11, no. 5, pp. 545–557, May 2002.
- BharathA.A.andJeffry.Ng,“A Steerable Complex Wavelet Construction and its Application to Image Denoising,” IEEE Trans. on. Image Processing,Vol.14, No.7, July 2005.
- Chang S.G., Yu B., and Vetterli M., “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Proc., vol. 9, no. 9, pp. 1532-1546, Sept. 2000.
- Dr.M.Venugopal Rao, Advanced wavelet techniques for improved image reconstruction in Computer Aided Tomography, osmania university, Phd Thesis,2011.
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