A Hybrid Method Based CT Image Denoising Using Nonsubsampled Contourlet and Curvelet Transforms
Computed tomography (CT) is one of the most widespread radio-logical tools for diagnosis purpose. To achieve good quality of CT images with low radiation dose has drawn a lot of attention to researchers. Hence, post-processing of CT images has become a ma
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Abstract Computed tomography (CT) is one of the most widespread radio-logical tools for diagnosis purpose. To achieve good quality of CT images with low radiation dose has drawn a lot of attention to researchers. Hence, post-processing of CT images has become a major concern in medical image processing. This paper presents a novel edge-preserving image denoising scheme where noisy CT images are denoised using nonsubsampled contourlet transform (NSCT) and curvelet transform separately. By estimating variance difference on both denoised images, final denoised CT image has been achieved using a variation-based weighted aggregation. The proposed scheme is compared with existing methods and it is observed that the performance of proposed method is superior to existing methods in terms of visual quality, image quality index (IQI), and peak signal-to-noise ratio (PSNR). Keywords Image denoising ⋅ Wavelet transform sampled contourlet transform ⋅ Thresholding
⋅ Curvelet transform ⋅ Nonsub-
1 Introduction In medical science, computed tomography (CT) is one of the important tools, which helps to provide the view of the human body’s internal structure in the form of digital images for diagnosis purpose. In computed tomography, X-rays are projected over the human body where soft and hard tissues are observed and other side, a detector is used to collect the observed data (raw data). Using Radon transform, these raw data are further mathematically computed to reconstruct the CT images. X-ray radiation dose beyond a certain level could increase the risk of cancer [1, 2]. Thus, it
M. Diwakar (✉) ⋅ M. Kumar Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India e-mail: [email protected] M. Kumar e-mail: [email protected] © Springer Science+Business Media Singapore 2017 B. Raman et al. (eds.), Proceedings of International Conference on Computer Vision and Image Processing, Advances in Intelligent Systems and Computing 459, DOI 10.1007/978-981-10-2104-6_51
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is an important to give lower amount of radiation during CT image reconstruction. However, reducing the radiation dose may increase the noise level of CT reconstructed images which may not be usable for diagnosis. Because of acquisition, transmission, and mathematical computation, the reconstructed CT images may be degraded in terms of noise. To surmount noisy problem, various methods have been investigated for noise suppression in CT images where wavelet transform-based denoising has achieved good results over the last few decades. In wavelet transform domain, wavelet coefficients are modified using various thresholding methods. For the modification of wavelet coefficients, numerous strategies have been proposed to improve denoising performance. These strategies can be broadly categorized into two categories: (i) intra-scale dependency-based denoising and (ii) inter-scale dependency-based denoising. In intra-scale dependency-based denoising [3–9], the wavelet coefficients are modified with in same scale. SureShrin
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