{"title":"CT Medical Images Denoising Based on New Wavelet Thresholding Compared with Curvelet and Contourlet","authors":"Amir Moslemi, Amir Movafeghi, Shahab Moradi","volume":106,"journal":"International Journal of Computer and Information Engineering","pagesStart":2167,"pagesEnd":2173,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10002440","abstract":"One of the most important challenging factors in\r\nmedical images is nominated as noise. Image denoising refers to the\r\nimprovement of a digital medical image that has been infected by\r\nAdditive White Gaussian Noise (AWGN). The digital medical image\r\nor video can be affected by different types of noises. They are\r\nimpulse noise, Poisson noise and AWGN. Computed tomography\r\n(CT) images are subjects to low quality due to the noise. Quality of\r\nCT images is dependent on absorbed dose to patients directly in such\r\na way that increase in absorbed radiation, consequently absorbed\r\ndose to patients (ADP), enhances the CT images quality. In this\r\nmanner, noise reduction techniques on purpose of images quality\r\nenhancement exposing no excess radiation to patients is one the\r\nchallenging problems for CT images processing. In this work, noise\r\nreduction in CT images was performed using two different\r\ndirectional 2 dimensional (2D) transformations; i.e., Curvelet and\r\nContourlet and Discrete Wavelet Transform (DWT) thresholding\r\nmethods of BayesShrink and AdaptShrink, compared to each other\r\nand we proposed a new threshold in wavelet domain for not only\r\nnoise reduction but also edge retaining, consequently the proposed\r\nmethod retains the modified coefficients significantly that result good\r\nvisual quality. Data evaluations were accomplished by using two\r\ncriterions; namely, peak signal to noise ratio (PSNR) and Structure\r\nsimilarity (Ssim).","references":"[1] Willi A. Kalender, \u201cDose in x-ray computed tomography\u2019\u2019, Physics in\r\nMedicine and Biology, Phys. Med. Biol. 59 (2014) R129\u2013R150.\r\n[2] Gonzalez. R. C and. Wood R.E, \u201cDigital Image Processing\u201d, 2nd edition,\r\nNew Jersey Prentice Hall, 2002.\r\n[3] R. R. Coifman and A. Sowa, \u201cCombining the calculus of variations and\r\nwavelets for image enhancement,\u201d Appl. Comput. Harmon. Anal., vol. 9,\r\nno. 1, pp. 1\u201318, Jul. 2000.\r\n[4] Jean-Luc Starck, Emmanuel J. Cand\u00e8s, and David L. Donoho, \u201cThe\r\nCurvelet Transform for Image Denoising\u2019\u2019. IEEE Transactions on Image\r\nProcessing, Vol. 11, No. 6, June 2002.\r\n[5] Minh N Do and Martin Vetterli, Fellow, IEEE, \u201cThe Contourlet\r\nTransform: An Efficient Directional Multiresolution Image\r\nRepresentation\u2019\u2019, IEEE Transaction On Image Processing, December\r\n2005.\r\n[6] Shao-Weidal, Yan-Kuisun, Xiao-Lin Tian, Ze-Sheng Tang, \u201cImage\r\nDesnoising Based On Complex Contourlet Transform\u2019\u2019, International\r\nConference on Wavelet Analysis and Pattern Recognition, Beijing,\r\nChina, 2-4 Nov .2007.\r\n[7] J. Hou , J. Tian, and J. Liu, \u201cAn improved Wienerchop algorithm for\r\nimage denoising\u2019\u2019, in Proc. of the IEEE International Conference on\r\nCommunications, Circuits and Systems (ICCCAS), vol. 2, pp. 838\u2013841,\r\nOct. 2005.\r\n[8] S. G. Chang, B. Yu, and M. Vetterli, \u201cSpatially adaptive wavelet\r\nthresholding with context modeling for image denoising,\u2019\u2019 IEEE Trans.\r\nImage Process., vol. 9, no. 9, pp. 1522\u20131531, 2000.\r\n[9] S. G. Chang, B. Yu, and M. Vettereli, \u201cAdaptive wavelet thresholding\r\nfor image denoising and compression,\u201d IEEE Trans. Image Processing,\r\nvol. 9, no. 9, pp. 1532\u20131546, 2000.\r\n[10] R. R. Coifman and A. Sowa, \u201cCombining the calculus of variations and\r\nwavelets for image enhancement,\u201d Appl. Comput. Harmon. Anal., vol. 9,\r\nno. 1, pp. 1\u201318, Jul. 2000.\r\n[11] Mantosh Biswas and Hari Om, \u201cAn Image Denoising Threshold\r\nEstimation Method\u201d Advances in Computer Science and its Applications\r\n(ACSA) 377 Vol. 2, No. 3, 2013, ISSN 2166-2924.\r\n[12] A. K. Velmurugan and Dr. R. Jagadeesh Kannan, \u201cWavelet Analysis For\r\nMedical Image Denoising Based on Thresholding Technique\u201d,\r\nInternational conference on current Trends in Engineering and\r\nTechnology, ICCTET\u201913, pp.213-215,2013.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 106, 2015"}