CT Image Denoising using DTCWT with Level Dependent Thresholding
|International Journal of Electronics and Communication Engineering|
|© 2018 by SSRG - IJECE Journal|
|Volume 5 Issue 8|
|Year of Publication : 2018|
|Authors : Harini N, Shaik Majeeth S, Aswanth Kumar G and Abinaya J|
How to Cite?
Harini N, Shaik Majeeth S, Aswanth Kumar G and Abinaya J, "CT Image Denoising using DTCWT with Level Dependent Thresholding," SSRG International Journal of Electronics and Communication Engineering, vol. 5, no. 8, pp. 14-21, 2018. Crossref, https://doi.org/10.14445/23488549/IJECE-V5I8P103
This work aims at denoising CT images corrupted by Gaussian noise. In general, several types of noise degrade the image during acquisition and transmission. In this work, the Gaussian noise that corrupts the CT image is removed by using Bilateral Filter combined with Dual Tree Complex Wavelet Transform (DTCWT). The noisy image is filtered by using Bilateral Filter which comprises of two Gaussian Filter. These two filters serve to obtain the filtered image with required spatial and range properties. The difference between the filtered image and the noisy image results in method noise. The method noise has details in addition with noise. Then DTCWT is applied on the method noise to estimate the high frequency details by suppressing the noise effectively. The estimated wavelet coefficients are level dependent thresholded using Bayes Shrink Method which preserves the coefficient of details and discards noisy coefficients. The thresholding is implemented for each DWT in the DTCWT by thresholding separately to obtain the details efficiently. Then the fine details are added with the filtered image to get the denoised image with details. The proposed method gives very good PSNR value when compared with the other existing techniques. Furthermore, the UIQI and SSIM of the de-noised image depict the quality of the image. The denoised image is also visually pleasing.
CT image; Bilateral Filter; DTCWT; Wavelet Thresholding; BayesShrink; Method Noise.
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