CT Medical Images Denoising Based on New Wavelet Thresholding Compared with Curvelet and Contourlet
One of the most important challenging factors in
medical images is nominated as noise. Image denoising refers to the
improvement of a digital medical image that has been infected by
Additive White Gaussian Noise (AWGN). The digital medical image
or video can be affected by different types of noises. They are
impulse noise, Poisson noise and AWGN. Computed tomography
(CT) images are subjects to low quality due to the noise. Quality of
CT images is dependent on absorbed dose to patients directly in such
a way that increase in absorbed radiation, consequently absorbed
dose to patients (ADP), enhances the CT images quality. In this
manner, noise reduction techniques on purpose of images quality
enhancement exposing no excess radiation to patients is one the
challenging problems for CT images processing. In this work, noise
reduction in CT images was performed using two different
directional 2 dimensional (2D) transformations; i.e., Curvelet and
Contourlet and Discrete Wavelet Transform (DWT) thresholding
methods of BayesShrink and AdaptShrink, compared to each other
and we proposed a new threshold in wavelet domain for not only
noise reduction but also edge retaining, consequently the proposed
method retains the modified coefficients significantly that result good
visual quality. Data evaluations were accomplished by using two
criterions; namely, peak signal to noise ratio (PSNR) and Structure
similarity (Ssim).
[1] Willi A. Kalender, “Dose in x-ray computed tomography’’, Physics in
Medicine and Biology, Phys. Med. Biol. 59 (2014) R129–R150.
[2] Gonzalez. R. C and. Wood R.E, “Digital Image Processing”, 2nd edition,
New Jersey Prentice Hall, 2002.
[3] R. R. Coifman and A. Sowa, “Combining the calculus of variations and
wavelets for image enhancement,” Appl. Comput. Harmon. Anal., vol. 9,
no. 1, pp. 1–18, Jul. 2000.
[4] Jean-Luc Starck, Emmanuel J. Candès, and David L. Donoho, “The
Curvelet Transform for Image Denoising’’. IEEE Transactions on Image
Processing, Vol. 11, No. 6, June 2002.
[5] Minh N Do and Martin Vetterli, Fellow, IEEE, “The Contourlet
Transform: An Efficient Directional Multiresolution Image
Representation’’, IEEE Transaction On Image Processing, December
2005.
[6] Shao-Weidal, Yan-Kuisun, Xiao-Lin Tian, Ze-Sheng Tang, “Image
Desnoising Based On Complex Contourlet Transform’’, International
Conference on Wavelet Analysis and Pattern Recognition, Beijing,
China, 2-4 Nov .2007.
[7] J. Hou , J. Tian, and J. Liu, “An improved Wienerchop algorithm for
image denoising’’, in Proc. of the IEEE International Conference on
Communications, Circuits and Systems (ICCCAS), vol. 2, pp. 838–841,
Oct. 2005.
[8] S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet
thresholding with context modeling for image denoising,’’ IEEE Trans.
Image Process., vol. 9, no. 9, pp. 1522–1531, 2000.
[9] S. G. Chang, B. Yu, and M. Vettereli, “Adaptive wavelet thresholding
for image denoising and compression,” IEEE Trans. Image Processing,
vol. 9, no. 9, pp. 1532–1546, 2000.
[10] R. R. Coifman and A. Sowa, “Combining the calculus of variations and
wavelets for image enhancement,” Appl. Comput. Harmon. Anal., vol. 9,
no. 1, pp. 1–18, Jul. 2000.
[11] Mantosh Biswas and Hari Om, “An Image Denoising Threshold
Estimation Method” Advances in Computer Science and its Applications
(ACSA) 377 Vol. 2, No. 3, 2013, ISSN 2166-2924.
[12] A. K. Velmurugan and Dr. R. Jagadeesh Kannan, “Wavelet Analysis For
Medical Image Denoising Based on Thresholding Technique”,
International conference on current Trends in Engineering and
Technology, ICCTET’13, pp.213-215,2013.
[1] Willi A. Kalender, “Dose in x-ray computed tomography’’, Physics in
Medicine and Biology, Phys. Med. Biol. 59 (2014) R129–R150.
[2] Gonzalez. R. C and. Wood R.E, “Digital Image Processing”, 2nd edition,
New Jersey Prentice Hall, 2002.
[3] R. R. Coifman and A. Sowa, “Combining the calculus of variations and
wavelets for image enhancement,” Appl. Comput. Harmon. Anal., vol. 9,
no. 1, pp. 1–18, Jul. 2000.
[4] Jean-Luc Starck, Emmanuel J. Candès, and David L. Donoho, “The
Curvelet Transform for Image Denoising’’. IEEE Transactions on Image
Processing, Vol. 11, No. 6, June 2002.
[5] Minh N Do and Martin Vetterli, Fellow, IEEE, “The Contourlet
Transform: An Efficient Directional Multiresolution Image
Representation’’, IEEE Transaction On Image Processing, December
2005.
[6] Shao-Weidal, Yan-Kuisun, Xiao-Lin Tian, Ze-Sheng Tang, “Image
Desnoising Based On Complex Contourlet Transform’’, International
Conference on Wavelet Analysis and Pattern Recognition, Beijing,
China, 2-4 Nov .2007.
[7] J. Hou , J. Tian, and J. Liu, “An improved Wienerchop algorithm for
image denoising’’, in Proc. of the IEEE International Conference on
Communications, Circuits and Systems (ICCCAS), vol. 2, pp. 838–841,
Oct. 2005.
[8] S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet
thresholding with context modeling for image denoising,’’ IEEE Trans.
Image Process., vol. 9, no. 9, pp. 1522–1531, 2000.
[9] S. G. Chang, B. Yu, and M. Vettereli, “Adaptive wavelet thresholding
for image denoising and compression,” IEEE Trans. Image Processing,
vol. 9, no. 9, pp. 1532–1546, 2000.
[10] R. R. Coifman and A. Sowa, “Combining the calculus of variations and
wavelets for image enhancement,” Appl. Comput. Harmon. Anal., vol. 9,
no. 1, pp. 1–18, Jul. 2000.
[11] Mantosh Biswas and Hari Om, “An Image Denoising Threshold
Estimation Method” Advances in Computer Science and its Applications
(ACSA) 377 Vol. 2, No. 3, 2013, ISSN 2166-2924.
[12] A. K. Velmurugan and Dr. R. Jagadeesh Kannan, “Wavelet Analysis For
Medical Image Denoising Based on Thresholding Technique”,
International conference on current Trends in Engineering and
Technology, ICCTET’13, pp.213-215,2013.
@article{"International Journal of Information, Control and Computer Sciences:70934", author = "Amir Moslemi and Amir Movafeghi and Shahab Moradi", title = "CT Medical Images Denoising Based on New Wavelet Thresholding Compared with Curvelet and Contourlet", abstract = "One of the most important challenging factors in
medical images is nominated as noise. Image denoising refers to the
improvement of a digital medical image that has been infected by
Additive White Gaussian Noise (AWGN). The digital medical image
or video can be affected by different types of noises. They are
impulse noise, Poisson noise and AWGN. Computed tomography
(CT) images are subjects to low quality due to the noise. Quality of
CT images is dependent on absorbed dose to patients directly in such
a way that increase in absorbed radiation, consequently absorbed
dose to patients (ADP), enhances the CT images quality. In this
manner, noise reduction techniques on purpose of images quality
enhancement exposing no excess radiation to patients is one the
challenging problems for CT images processing. In this work, noise
reduction in CT images was performed using two different
directional 2 dimensional (2D) transformations; i.e., Curvelet and
Contourlet and Discrete Wavelet Transform (DWT) thresholding
methods of BayesShrink and AdaptShrink, compared to each other
and we proposed a new threshold in wavelet domain for not only
noise reduction but also edge retaining, consequently the proposed
method retains the modified coefficients significantly that result good
visual quality. Data evaluations were accomplished by using two
criterions; namely, peak signal to noise ratio (PSNR) and Structure
similarity (Ssim).", keywords = "Computed Tomography (CT), noise reduction,
curve-let, contour-let, Signal to Noise Peak-Peak Ratio (PSNR),
Structure Similarity (Ssim), Absorbed Dose to Patient (ADP).", volume = "9", number = "10", pages = "2174-6", }