Image denoising using Wavelets: A powerful tool to overcome some limitations in nuclear imaging

Khlifa, Nawrès; Hamrouni, Kamel; Ellouze, Noureddine

doi:10.1109/ictta.2006.1684530

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…[ 6 ] Although image filtering in the frequency domain is routinely used in nuclear medicine, recently image denoising in the wavelet domain is becoming popular. [ 7 8 9 10 11 ]…”

Section: Introductionmentioning

confidence: 99%

Comparison of noise estimation methods used in denoising 99mTc-sestamibi parathyroid images using wavelet transform

Pandey

Sharma

et al. 2021

World J Nucl Med

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The objective of this study was to compare the performance of variance, median absolute deviation, and the square of median absolute deviation methods of noise estimation in denoising of 99m Tc-sestamibi parathyroid images using wavelet transform. Sixty-eight 99m Tc-sestamibi parathyroid images including 33 images acquired at zoom 1.0 and 35 acquired at zoom 2.0 were denoised using the wavethresh package in R. The image decomposition and reconstruction method discrete wavelet transform, wavelet filter db4, shrinkage method hard, and thresholding policy universal were used. The noise estimation in the process was made using var, mad and madmad functions, which use variance, mean absolute deviation, and the square of mean absolute deviation, respectively. The quality of denoised images was assessed both qualitatively and quantitatively. A nonparametric two-sample Kolmogorov–Smirnov test was applied to find whether the difference in image quality produced by these three noise estimation methods was significant at 95% confidence. Noise estimation using madmad function produced the best quality denoised image. Further, the quality of the denoised image using madmad function was significantly better than the quality of the denoised image obtained with var or mad function ( P = 1). The estimation of noise using madmad functions in wavelet transforms provides the best-denoised image for both zoom 1.0 and zoom 2.0 99m Tc-sestamibi parathyroid images.

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“…[ 6 ] Although image filtering in the frequency domain is routinely used in nuclear medicine, recently image denoising in the wavelet domain is becoming popular. [ 7 8 9 10 11 ]…”

Section: Introductionmentioning

confidence: 99%

Comparison of noise estimation methods used in denoising 99mTc-sestamibi parathyroid images using wavelet transform

Pandey

Sharma

et al. 2021

World J Nucl Med

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“…In a scintigraphic image, the pixel value represents the radiation counting by the detector and contains a discrete value. These discrete values follow a distribution and this distribution is responsible for the presence of noise in the images [10][11][12]. So, image processing is needed for noise removal or reduction [14].…”

Section: Introductionmentioning

confidence: 99%

“…Image de-noising using wavelets shows the capability of fulfilling the compromise between smoothing and keeping important features [11]. Noise reduction in nuclear medicine can be proposed using wavelet transform [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Planar Scintigraphy Image De-Noising Using Coiflet Wavelet

Puspitasari

Ningsih

Ridwan

et al. 2021

gnd

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The planar scintigraphic image usually has poor resolution and contains noise. This noise can be removed using the coiflet wavelet method so that the image quality gets better. This coiflet wavelet method is a noise reduction method based on frequency analysis. The planar scintigraphy image is the reconstructed image of the gamma radiation count data (phantom with the Cs-137 source in it). The original image is 15×15 pixel. Before the de-noising process, the image went through an interpolation process, which is to increase the pixel size of the image. The original image enlarged to 70×70, 480×480, and 1200×1200 pixel. After de-noising with coiflet wavelet, the image quality is measured based on MSE and PSNR parameters. The resulting images are quite good, with MSE values are close to zero and PSNR values of more than 60 dB. The smaller the MSE and the bigger the PSNR, is getting the better the image quality. In this study, the results show that the 1200×1200 pixel image has the best quality. It means that the image enlargement process has a good effect on the de-noising process, especially if the original image has a low resolution.

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“…In nuclear medicine, scintigraphic images are used for investigation of some organs, regardless of their poor resolution. Poisson noise is one of the important sources of deterioration in scintigraphic images [2]. Numerous techniques have already been used for reduction of Poisson noise in scintigraphic and Single Photon Emission Computed Tomography (SPECT) images.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Trimmed Mean Autoregressive Model for Reduction of Poisson Noise in Scintigraphic Images

Khan

Shahid

Ullah

et al. 2018

IIUMEJ

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A 2-D Adaptive Trimmed Mean Autoregressive (ATMAR) model has been proposed for denoising of medical images corrupted with poisson noise. Unfiltered images are divided into smaller chunks and ATMAR model is applied on each chunk separately. In this paper, two 5x5 windows with 40% overlapping are used to predict the center pixel value of the central row. The AR coefficients are updated by sliding both windows forward with 60% shift. The same process is repeated to scan the entire image for prediction of a new denoised image. The Adaptive Trimmed Mean Filter (ATMF) eradicates the lowest and highest variations in pixel values of the ATMAR model denoised image and also average out the remaining neighborhood pixel values. Finally, power-law transformation is applied on the resultant image of the ATMAR model for contrast stretching. Image quality is judged in terms of correlation, Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) of the image with latest denoising techniques. The proposed technique showed an efficient way to scale down poisson noise in scintigraphic images on a pixel-by-pixel basis.

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Image denoising using Wavelets: A powerful tool to overcome some limitations in nuclear imaging

Cited by 9 publications

References 20 publications

Comparison of noise estimation methods used in denoising 99mTc-sestamibi parathyroid images using wavelet transform

Comparison of noise estimation methods used in denoising 99mTc-sestamibi parathyroid images using wavelet transform

Planar Scintigraphy Image De-Noising Using Coiflet Wavelet

Adaptive Trimmed Mean Autoregressive Model for Reduction of Poisson Noise in Scintigraphic Images

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