Median modified wiener filter for improving the image quality of gamma camera images

Park, Chan Rok; Kang, Seong-Hyeon; Lee, Youngjin

doi:10.1016/j.net.2020.03.022

Cited by 35 publications

(13 citation statements)

References 12 publications

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“…Various image denoising techniques have been used to remove noise from scintigraphic images. These include linear filters [26] and order statistic filters such as a median filter in the spatial domain, [27] and Butterworth filter [28] and Wiener filter in the frequency domain, [29] deep convolution neural network, [30] median modified Wiener filter technique, [31] a blind-deconvolution framework after a noise-reduction algorithm based on a nonlocal mean [32] etc.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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show abstract

“…Nevertheless, conventional denoising methods have a limitation in reducing sharpness with noise reduction. Cannistraci et al [ 50 ] introduced the median modified Wiener filter (MMWF), and Park et al [ 51 ] showed improved image performance using the MMWF in gamma camera images. It was confirmed that the MMWF is effective in suppressing the noise component while preserving the outline of the object as much as possible.…”

Section: Methodsmentioning

confidence: 99%

Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease

et al. 2022

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Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely used to predict AD using a deep learning model. However, the effects of noise and blurring on 18F-FDG PET images were not considered. The performance of a classification model trained using raw, deblurred (by the fast total variation deblurring method), or denoised (by the median modified Wiener filter) 18F-FDG PET images without or with cropping around the limbic system area using a 3D deep convolutional neural network was investigated. The classification model trained using denoised whole-brain 18F-FDG PET images achieved classification performance (0.75/0.65/0.79/0.39 for sensitivity/specificity/F1-score/Matthews correlation coefficient (MCC), respectively) higher than that with raw and deblurred 18F-FDG PET images. The classification model trained using cropped raw 18F-FDG PET images achieved higher performance (0.78/0.63/0.81/0.40 for sensitivity/specificity/F1-score/MCC) than the whole-brain 18F-FDG PET images (0.72/0.32/0.71/0.10 for sensitivity/specificity/F1-score/MCC, respectively). The 18F-FDG PET image deblurring and cropping (0.89/0.67/0.88/0.57 for sensitivity/specificity/F1-score/MCC) procedures were the most helpful for improving performance. For this model, the right middle frontal, middle temporal, insula, and hippocampus areas were the most predictive of AD using the class activation map. Our findings demonstrate that 18F-FDG PET image preprocessing and cropping improves the explainability and potential clinical applicability of deep learning models.

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“…Experimental process: image data collection, image pre‐processing, establishment of neural network algorithm model, train neural network, and accuracy verification of example experiments. Among them, image pre‐processing uses Wiener filtering to suppress image noise (Jamaludin et al, 2021; Park et al, 2020; Brown et al, 2019), and blur enhancement to improve image contrast (Li et al, 2020; Golestan et al, 2014; Cao et al, 2008; Li et al, 2011), and then extract texture and fractal features from the pre‐processed image, and finally input artificial model. The segmentation algorithm function was used to train the image neural network model (Saha et al, 2011; Kemnitz & Eckstein, 2017).…”

Section: Methodsmentioning

confidence: 99%

Image segmentation algorithm of lung cancer based on neural network model

Zhang

et al. 2021

Expert Systems

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To explore the application of neural network algorithm model in lung cancer imaging, and provide reference for the application and development of artificial neural network (ANN) algorithm model in lung cancer medical mirroring, so as to promote the development of ANN in this field. Meanwhile, it is hoped that the application of neural network algorithms in medical imaging can improve the survival rate and cure rate of lung cancer. In this study, an ANN algorithm model was selected to establish a lung cancer recognition model. After determining the lung cancer lesion area, the image segmentation algorithm was used to separately display the lung cancer lesion area, and a comparison experiment was designed to verify the accuracy of the model. ANNs were used to identify lung cancer, which can be concluded that the accuracy is 94.6%, the sensitivity is 95.7%, and the specificity is 93.5%. By combining image retrieval methods with lung cancer image segmentation algorithms, the lesion area of lung cancer can be clearly displayed. Therefore, the lung cancer image segmentation algorithm based on the neural network model has good recognition performance. This research can provide reference for the application of neural network algorithm model in the field of cancer diagnosis and treatment.

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Median modified wiener filter for improving the image quality of gamma camera images

Cited by 35 publications

References 12 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

Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease

Image segmentation algorithm of lung cancer based on neural network model

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