Adaptive Wiener Filter based on Gaussian Mixture Distribution Model for Denoising Chest X-ray CT Image

Tabuchi, Motohiro; Yamane, Nobumoto; Morikawa, Yoshitaka

doi:10.6009/jjrt.64.563

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…Spatial domain Linear and non-linear filters 13 20,21 Variation methods 11 13,[22][23][24][25][26][27][28][29][30][31] Dictionary learning method 7 [32][33][34][35][36][37][38] Bilateral and non-local means filters 13 2,12,[39][40][41][42][43][44][45][46][47][48][49] Transform domain…”

Section: Number Of Studiesmentioning

confidence: 99%

“…Wavelet based denoising 5 11,[16][17][18]50 Threshold estimation 2 10,51 Shrinkage rules 2 17,19 Intra and inter scale dependencies based denoising 2 52,53 Image denoising based on extended versions of transform 6 14,21,51,[54][55][56] Block-matching and 3D filtering (BM3D) 4 15,25,57,58 image patches from a set of training images and uses this dictionary to denoise new images. This method can effectively reduce noise while preserving image details and structures.…”

Section: Number Of Studiesmentioning

confidence: 99%

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CT image denoising methods for image quality improvement and radiation dose reduction

Sadia,

Chen,

Zhang

2024

J Applied Clin Med Phys

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With the ever‐increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low‐dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)‐based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL‐based models.

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Section: Number Of Studiesmentioning

confidence: 99%

Section: Number Of Studiesmentioning

confidence: 99%

CT image denoising methods for image quality improvement and radiation dose reduction

Sadia,

Chen,

Zhang

2024

J Applied Clin Med Phys

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A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images

Bhowal

Sen

Sarkar

2021

J Ambient Intell Human Comput

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The world is still under the threat of different strains of the coronavirus and the pandemic situation is far from over. The method, that is widely used for the detection of COVID-19 is Reverse Transcription Polymerase chain reaction (RT-PCR), which is a time-consuming method and is prone to manual errors, and has poor precision. Although many nations across the globe have begun the mass immunization procedure, the COVID-19 vaccine will take a long time to reach everyone. The application of artificial intelligence (AI) and computer-aided diagnosis (CAD) has been used in the domain of medical imaging for a long period. It is quite evident that the use of CAD in the detection of COVID-19 is inevitable. The main objective of this paper is to use convolutional neural network (CNN) and a novel feature selection technique to analyze Chest X-Ray (CXR) images for the detection of COVID-19. We propose a novel two-tier feature selection method, which increases the accuracy of the overall classification model used for screening COVID-19 CXRs. Filter feature selection models are often more effective than wrapper methods as wrapper methods tend to be computationally more expensive and are not useful for large datasets dealing with a large number of features. However, most filter methods do not take into consideration how a group of features would work together, rather they just look at the features individually and decide on a score. We have used approximate Shapley value, a concept of Coalition game theory, to deal with this problem. Further, in the case of a large dataset, it is important to work with shorter embeddings of the features. We have used CUR decomposition and Nystrom sampling to further reduce the feature space. To check the efficacy of this two-tier feature selection method, we have applied it to the features extracted by three standard deep learning models, namely VGG16 , Xception and InceptionV3 , where the features have been extracted from the CXR images of COVID-19 datasets and we have found that the selection procedure works quite well for the features extracted by Xception and InceptionV3 . The source code of this work is available at https://github.com/subhankar01/covidfs-aihc .

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A study of the possibility of fat quantification diagnosis technology in ultrasound image using machine learning

2024

J. Korean Phys. Soc.

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Adaptive Wiener Filter based on Gaussian Mixture Distribution Model for Denoising Chest X-ray CT Image

Cited by 6 publications

References 5 publications

CT image denoising methods for image quality improvement and radiation dose reduction

CT image denoising methods for image quality improvement and radiation dose reduction

A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images

A study of the possibility of fat quantification diagnosis technology in ultrasound image using machine learning

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