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A scheme for X-ray medical image denoising using sparse representations

Evmorfia Adamidi, Evangelos Vlachos, Aris Dermitzakis, Kostas Berberidis, Nicolas Pallikarakis
2013 13th IEEE International Conference on BioInformatics and BioEngineering  
This paper addresses the problem of noise removal in X-ray medical images. A novel scheme for image denoising is proposed, by leveraging recent advances in sparse and redundant representations.  ...  The new scheme has been tested with both artificial and real X-ray images and it turns out that it may offer superior denoising results as compared to other existing methods.  ...  A Scheme for X-ray Medical Image Denoising using Sparse Representations III.  ... 
doi:10.1109/bibe.2013.6701544 dblp:conf/bibe/AdamidiVDBP13 fatcat:2crkn5gxqjhw3mxtpojreu4n5m

Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images

Mehmet Yamac, Mete Ahishali, Aysen Degerli, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Moncef Gabbouj
2021 IEEE Transactions on Neural Networks and Learning Systems  
X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis.  ...  with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images  ...  The most common tool that medical experts use for both diagnostic and monitoring the course of the disease is X-ray imaging.  ... 
doi:10.1109/tnnls.2021.3070467 fatcat:5ry2stzkijd7lmryfq3eyplzzy

Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images [article]

Mehmet Yamac, Mete Ahishali, Aysen Degerli, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Moncef Gabbouj
2020 arXiv   pre-print
X-ray imaging is a common and easily accessible tool that has great potential for Covid-19 diagnosis. In this study, we propose a novel approach for Covid-19 recognition from chest X-ray images.  ...  when using them for Covid-19 detection.  ...  The most common tool that medical experts use for both diagnostic and monitoring the course of the disease is X-ray imaging.  ... 
arXiv:2005.04014v1 fatcat:o4ukzhrbtjb6jc6ey5xpnsfpui

PET image reconstruction and denoising on hexagonal lattices

Syed Tabish Abbas, Jayanthi Sivaswamy
2015 2015 IEEE International Conference on Image Processing (ICIP)  
We use filtered back projection for reconstruction, followed by a sparse dictionary based denoising and compare noise-free reconstruction on the Square and Hexagonal lattices.  ...  Nuclear imaging modalities like Positron emission tomography (PET) are characterized by a low SNR value due to the underlying signal generation mechanism.  ...  The quality of the reconstructed image plays a key role in its usefulness as a basis for medical diagnostics. Better image quality naturally facilitates more accurate diagnosis.  ... 
doi:10.1109/icip.2015.7351451 dblp:conf/icip/AbbasS15 fatcat:czhavkbwg5hhvgbiamsj3dfrzy

Nonconvex L_ 1/2-Regularized Nonlocal Self-similarity Denoiser for Compressive Sensing based CT Reconstruction [article]

Yunyi Li
2022 arXiv   pre-print
In this paper, we develop a L_ 1/2-regularized nonlocal self-similarity (NSS) denoiser for CT reconstruction problem, which integrates low-rank approximation with group sparse coding (GSC) framework.  ...  Concretely, we first split the CT reconstruction problem into two subproblems, and then improve the CT image quality furtherly using our L_ 1/2-regularized NSS denoiser.  ...  Acknowledgments The authors appreciate the anonymous reviewers for their extensive and informative comments for the improvement of this manuscript.  ... 
arXiv:2205.07185v1 fatcat:2zqy4mxho5cslnedlwtwjmf65i

Sparse and redundant signal representations for x-ray computed tomography [article]

Davood Karimi
2019 arXiv   pre-print
In the past decade, patch-based models have emerged as one of the most effective models for natural images.  ...  algorithms for computed tomography (CT).  ...  The prior assumption in denoising using a dictionary D is that every patch in the image has a sparse representation in D.  ... 
arXiv:1912.03379v1 fatcat:44xkielnmjbmnkf73dntlvtyge

Augmented noise learning framework for enhancing medical image denoising

Swati Rai, Jignesh S. Bhatt, S. K. Patra
2021 IEEE Access  
Deep learning attempts medical image denoising either by directly learning the noise present or via first learning the image content.  ...  The input images are presented to patch-based DL to indirectly learn the noise via sparse representation while given to the DRN to directly learn the noise.  ...  They also thank to a team of qualified technicians, including Vikas Patel, Gujarat, India, for providing practical information on medical imaging techniques and to Dr.  ... 
doi:10.1109/access.2021.3106707 fatcat:uzz5bw3ayvckvlnxzdly5rd3p4

Security assured CNN-Based Model for Reconstruction of Medical Images on the Internet of Healthcare Things

Sujeet More, Jimmy Singla, Sahil Verma, Kavita, Uttam Ghosh, Joel J. P. C. Rodrigues, A. S. M. Sanwar Hosen, In-Ho Ra
2020 IEEE Access  
In this research, an innovative and improvised denoising technique is implemented that applies a sparse aware with convolution neural network (SA_CNN) for investigating various medical modalities.  ...  Medical Imaging is the most significant technique that constitutes information needed to diagnose and make the right decisions for treatment.  ...  Muchchandi, Director and Chief Radiologist, Vagus Super Specialist Hospital, Bengaluru, India, for providing the medical image datasets.  ... 
doi:10.1109/access.2020.3006346 fatcat:uyi7gqcvrjaq3c4ifrcs6u3tdi

Image Reconstruction by Splitting Deep Learning Regularization from Iterative Inversion [chapter]

Jiulong Liu, Tao Kuang, Xiaoqun Zhang
2018 Lecture Notes in Computer Science  
The proposed approach involves only techniques of conventional image reconstruction and usual image representation/denoising deep network learning, without a specifically designed and complicated network  ...  structures for a certain physical forward operator.  ...  We often consider a linear imaging system with a forward operator A, for example partial 2D Fourier transform for MRI and X-ray transform for CT.  ... 
doi:10.1007/978-3-030-00928-1_26 fatcat:qefyg7boqjc4lkrsg62kbvsf4q

Sparse MR Image Reconstruction Considering Rician Noise Models: A CNN Approach

M. V. R. Manimala, C. Dhanunjaya Naidu, M. N. Giri Prasad
2020 Wireless personal communications  
The proposed algorithm employs a convolutional neural network (CNN) to denoise MR images corrupted with Rician noise.  ...  In this work, we develop a novel framework to reconstruct MR images with high speed and visual quality from noisy sparse k-space data.  ...  Hence, in this work we present the concept of denoising MR images based on sparse representation with CNN methods.  ... 
doi:10.1007/s11277-020-07725-0 pmid:32836885 pmcid:PMC7417787 fatcat:4qnwocia6bathamuirypxjdypq

ASSESSMENT OF RESTORATION METHODS OF X-RAY IMAGES WITH EMPHASIS ON MEDICAL PHOTOGRAMMETRIC USAGE

S. Hosseinian, H. Arefi
2016 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
For this purpose, after demonstrating the properties of medical X-ray images, the more efficient and recommended methods for restoration of X-ray images would be described and assessed.  ...  Nowadays, various medical X-ray imaging methods such as digital radiography, computed tomography and fluoroscopy are used as important tools in diagnostic and operative processes especially in the computer  ...  Hosseinian for their help in providing input data for the tests.  ... 
doi:10.5194/isprsarchives-xli-b5-835-2016 fatcat:czyojoxphvfwlczvrut5r2u7ci

ASSESSMENT OF RESTORATION METHODS OF X-RAY IMAGES WITH EMPHASIS ON MEDICAL PHOTOGRAMMETRIC USAGE

S. Hosseinian, H. Arefi
2016 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
For this purpose, after demonstrating the properties of medical X-ray images, the more efficient and recommended methods for restoration of X-ray images would be described and assessed.  ...  Nowadays, various medical X-ray imaging methods such as digital radiography, computed tomography and fluoroscopy are used as important tools in diagnostic and operative processes especially in the computer  ...  Hosseinian for their help in providing input data for the tests.  ... 
doi:10.5194/isprs-archives-xli-b5-835-2016 fatcat:gkwxlirxwvdfvmviu2crygwh5m

Application of Discrete Multi-wavelet Transform in Denoising of Mammographic Images

Smriti Bhatnagar, Roop Chand Jain
2016 Indian Journal of Science and Technology  
A modified approach for de noising of mammographic images using Multi Wavelet Transform has been proposed with four different thresholding techniques.  ...  Methods/Statistical Analysis: In this paper de noising algorithms in wavelet domain for mammographic images (used for detection of breast cancer in women) are considered.  ...  Mammography is low energy X ray of breast, used for early detection of Breast Cancer in women.  ... 
doi:10.17485/ijst/2016/v9i48/103384 fatcat:pbim3hcqdnfcfjlbymy3wcpbgy

Performance Evaluation of Super-Resolution Methods Using Deep-Learning and Sparse-Coding for Improving the Image Quality of Magnified Images in Chest Radiographs

Kensuke Umehara, Junko Ota, Naoki Ishimaru, Shunsuke Ohno, Kentaro Okamoto, Takanori Suzuki, Takayuki Ishida
2017 Open Journal of Medical Imaging  
Materials and Methods: A total of 247 chest X-rays were sampled from the JSRT database, then divided into 93 training cases with non-nodules and 154 test cases with lung nodules.  ...  Of the two tested schemes, the SRCNN scheme processed the images fastest; thus, SRCNN could be clinically superior for processing radiographs in terms of both image quality and processing speed.  ...  , but to real-time X-ray imaging as well.  ... 
doi:10.4236/ojmi.2017.73010 fatcat:uhnmio6ulzf7hln2qbg6cpnlym

Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning [article]

Saiprasad Ravishankar, Jong Chul Ye, Jeffrey A. Fessler
2019 arXiv   pre-print
The first type tended to be analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on  ...  A second type is iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics.  ...  For example, a recent work [110] learned efficient double sparse transforms for image denoising, wherein W = BΦ, with B being a sparse matrix and Φ being an analytical transform (e.g., DCT) with a fast  ... 
arXiv:1904.02816v2 fatcat:ehahzrib2ff3dl5yl6pa7xpf24
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