Manoj Diwakar

CT image noise reduction based on adaptive wiener filtering with Wavelet packet thresholding

Computed Tomography (CT) is streamlined in radiological diagnostics and has become an imperative tool in medical examinations. The difficulty that arises with the demand is to improve CT image quality without increasing dose. In this paper, Wavelet based noise reduction technique is proposed to improve image quality where adaptive Wiener filtering and Wavelet Packet Threshold (WPT) algorithm are applied. The Noisy CT image is decomposed using DWT, where approximation part is filtered using WPT algorithm and detail part is filtered by the adaptive Wiener filtering. By using the level dependent, the wavelet packet tree coefficients are calculated using optimal linear interpolation shrinkage function. Denoised image is acquired using wavelet packet reconstruction and inverse DWT. The value of the peak signal to noise ratio (PSNR) is used as the measure of image visual quality. Experimental results demonstrate that the proposed method improves the image visual quality in respect of noise removal and edge preservation.

CT image denoising based on complex wavelet transform using local adaptive thresholding and Bilateral filtering

Computed Tomography (CT) is one of the most widespread radiological tools in medical diagnostics. 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 based on Dual-tree Complex Wavelet Transform (DT-CWT), Bilateral filtering and a locally adaptive thresholding method. The noisy image is decomposed into Complex Wavelet coefficients through a Dual-tree Complex Wavelet Transform. Low-pass subbands are modified using Bilateral filter. High pass subbands are modified using locally adaptive thresholding based on interscale statistical dependency, where the noise variance of noisy wavelet coefficients are estimated using a robust median estimator. Denoised image is retrieved using inverse DT-CWT. The proposed scheme is compared with existing methods. It is observed that performance of proposed method is superior than existing methods in terms of visual quality, Image Quality Index (IQI) and Peak Signal-to-noise Ratio (PSNR).

Edge preservation based CT image denoising using Wiener filtering and thresholding in wavelet domain

Computed tomography (CT) is among one of the important tools which helps to depict the complications of human body. The CT images help to identify the medical relevant details for diagnosis purpose. Due to presence of noise, a medical image may not give the accurate analysis which may harmful for the patients. This article proposed a scheme based on Wiener filtering in wavelet domain. In proposed scheme, CT image is denoised using concept of Wiener filtering and method noise in wavelet domain. In proposed scheme, CT image is denoised using concept of Wiener filtering and method noise in wavelet domain. The resultant image of proposed methodology gives noise suppressed as well as edge preserved image. To measure the performance of proposed scheme, the performance metrics (PSNR, SSIM) are calculated and also compared with some existing schemes. Experimental evaluation indicates that the quality of CT images is enhanced in terms of noise reduction as well as structure preservation.

CT image denoising using NLM and correlation-based wavelet packet thresholding

The impact of radiation dose is directly related to the quality of computed tomography (CT) images. Low-dose CT images are degraded with the noise and other factors. Noise reduction methods are very helpful to enhance the noisy CT images with a possibility to increase the signal-to-noise ratio (SNR) and have a scope for further reduction of radiation dose. In this study, a denoising scheme is proposed which is applicable only for two identical images with uncorrelated noise. In the proposed scheme, a non-local means (NLM) filter is used to denoise the first input image and a wavelet packet thresholding to denoise the second input image. Results of NLM filter are analysed and found excellent for noise suppression but missing the small structures of the input image. To recover that, the proposed scheme is using correlation-based wavelet packet thresholding. The final outcomes of the proposed scheme are excellent in terms of noise suppression and structure preservation. The proposed scheme is compared with existing methods and it is observed that performance of the proposed method is superior to existing methods in terms of visual quality, image quality index, peak SNR and entropy difference.

A review on CT image noise and its denoising

Abstract CT imaging is widely used in medical science over the last decades. The process of CT image reconstruction depends on many physical measurements such as radiation dose, software/hardware. Due to statistical uncertainty in all physical measurements in Computed Tomography, the inevitable noise is introduced in CT images. Therefore, edge-preserving denoising methods are required to enhance the quality of CT images. However, there is a tradeoff between noise reduction and the preservation of actual medical relevant contents. Reducing the noise without losing the important features of the image such as edges, corners and other sharp structures, is a challenging task. Nevertheless, various techniques have been presented to suppress the noise from the CT scanned images. Each technique has their own assumptions, merits and limitations. This paper contains a survey of some significant work in the area of CT image denoising. Often, researchers face difficulty to understand the noise in CT images and also to select an appropriate denoising method that is specific to their purpose. Hence, a brief introduction about CT imaging, the characteristics of noise in CT images and the popular methods of CT image denoising are presented here. The merits and drawbacks of CT image denoising methods are also discussed.

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 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).

Edge Preservation Based CT Image Denoising Using Wavelet and Curvelet Transforms

Computed tomography (CT) is a well-known medical radiological tool to diagnose the human body. Radiation dose is one of the major factors, which affects the quality of CT images. High radiation dose may improve the quality of image in terms of reducing noise, but it may be harmful for the patients. Due to low radiation dose, reconstructed CT images are noisy. To improve quality of noisy CT image, a postprocessing method is proposed. The goal of proposed scheme is to reduce the noise as much as possible by preserving the edges. The scheme is divided into two phases. In first phase, wavelet transform based denoisng is performed using bilateral filtering and thresholding. In second phase, a method noise thresholding based on curvelet transform is performed using the outcome of first phase. The proposed scheme is compared with existing methods. From experimental evaluation, it is observed that the performance of proposed scheme is superior to existing methods in terms of visual quality, PSNR and image quality index (IQI).