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Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

Nan Lin, Junhai Jiang, Shicheng Guo, Momiao Xiong, Rongling Wu
2015 PLoS ONE  
The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster  ...  Recently, randomized algorithms have received a great deal of attention in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image cluster analysis.  ...  The authors wish to acknowledge the contributions of the research institutions, study investigators, field staff and study participants in creating the TCGA datasets for biomedical research.  ... 
doi:10.1371/journal.pone.0132945 pmid:26196383 pmcid:PMC4510534 fatcat:qv3rnfgjobgwxlqjbanletkdwm

Fast clustering for scalable statistical analysis on structured images [article]

Bertrand Thirion, Andrés Hoyos-Idrobo (NEUROSPIN, PARIETAL), Jonas Kahn
2015 arXiv   pre-print
Specifically, we investigate the use of alternate schemes, based on fast clustering, that are well suited for signals exhibiting a strong spatial structure, such as anatomical and functional brain images  ...  Our contribution is twofold: i) we propose a linear-time clustering scheme that bypasses the percolation issues inherent in these algorithms and thus provides compressions nearly as good as traditional  ...  For instance, given a problem with n samples and p dimensions the most classical linear algorithms (such as Principal components analysis) have complexity O(min(p 2 n, n 2 p), which becomes exorbitant  ... 
arXiv:1511.04898v1 fatcat:wycyl2bgqnbdtakqdxxteq5uz4

Low-rank modeling and its applications in medical image analysis

Xiaowei Zhou, Weichuan Yu, Harold H. Szu
2013 Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI  
This facilitation, however, is often qualitative instead of quantitative due to the analysis challenges associated with medical images such as low signal-to-noise ratio, signal dropout, and large variations  ...  Computer-aided medical image analysis has been widely used in clinics to facilitate objective disease diagnosis.  ...  Robust Principal Component Analysis While PCA is optimal in the case of i.i.d.  ... 
doi:10.1117/12.2017684 fatcat:llll5olpnfbpvaamvomul346p4

A Survey on Image Denoising Techniques

S. Preethi, D. Narmadha
2012 International Journal of Computer Applications  
Image processing is an important charge in image denoising as a process and component in various other process There are many ways to denoise an image.The ultimate idea of this paper is to acquiesce better  ...  This paper is compared with three methods NL Means, NL-PCA, and DCT.PSNR and SSIM are used for quantitative study of denoising methods. General Terms -Image denoising, Quality, Rician noise.  ...  Performance Analysis For Non Linear Filering Algorithm For Underwater Images They use 5 algorithms for removing 3types of noises: In median filter original and result pixel filter has same pixels In component  ... 
doi:10.5120/9288-3488 fatcat:grxq7thlnvbt7nibm6njnzknei

A Tour of Unsupervised Deep Learning for Medical Image Analysis [article]

Khalid Raza, Nripendra Kumar Singh
2018 arXiv   pre-print
Future research opportunities and challenges of unsupervised techniques for medical image analysis have also been discussed.  ...  In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical imaging and image analysis.  ...  Almas Jabeen, and Mr. Nisar Wani for necessary support. Conflict of Interest Statement Authors declare that there is no any conflict of interest in the publication of this manuscript.  ... 
arXiv:1812.07715v1 fatcat:4dd75wfhvnf7db3v72575tikoi

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

Lyu, Peng, Pu, Yang, Wang, Peng
2020 Remote Sensing  
In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image  ...  The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed.  ...  At the same time, principal component analysis can be used to obtain sparse components, so as to obtain sparse images with cirrus clouds.  ... 
doi:10.3390/rs12010142 fatcat:gxnoa77yaverniwkzpa5nduvy4

Image Deblurring Via Total Variation Based Structured Sparse Model Selection

Liyan Ma, Tieyong Zeng
2015 Journal of Scientific Computing  
Numerical experimental results show that the proposed algorithm achieves competitive performance. As a generalization, we give a modified model for deblurring under salt-and-pepper noise.  ...  In this talk, we study the image deblurring problem based on sparse representation over learned dictionary which leads to promising performance in image restoration in recent years.  ...  Fast blockwise filtering with noise analysis. With the help of the principal component analysis of image noise, the parameters of image noise are estimated.  ... 
doi:10.1007/s10915-015-0067-7 fatcat:cbpymhfkrrfz5cpqci4kjyqzbe

Application of Sparse Representation in Bioinformatics

Shuguang Han, Ning Wang, Yuxin Guo, Furong Tang, Lei Xu, Ying Ju, Lei Shi
2021 Frontiers in Genetics  
Researchers have not only extended the sparse representation of a signal to image presentation, but also applied the sparsity of vectors to that of matrices.  ...  , low-rank sparse representations to analyze and process gene expression profiles, and an introduction to related cancers and gene expression profile database.  ...  ACKNOWLEDGMENTS We thank Maxine Garcia, PhD, from Liwen Bianji (Edanz) (www. liwenbianji.cn/) for editing the English text of a draft of this manuscript.  ... 
doi:10.3389/fgene.2021.810875 pmid:34976030 pmcid:PMC8715914 fatcat:fgioonmcffd7bftmvpoghbqucy

Face recognition by independent component analysis

M.S. Bartlett, J.R. Movellan, T.J. Sejnowski
2002 IEEE Transactions on Neural Networks  
Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods.  ...  as random variables and the images as outcomes.  ...  ICA and Cluster Analysis: Cluster analysis is a technique for finding regions in -dimensional space with large concentrations of data. These regions are called "clusters."  ... 
doi:10.1109/tnn.2002.804287 pmid:18244540 pmcid:PMC2898524 fatcat:xnwinaxxt5c7hdj3x7aobcn2my

Radiological images and machine learning: Trends, perspectives, and prospects

Zhenwei Zhang, Ervin Sejdić
2019 Computers in Biology and Medicine  
This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies  ...  imaging and positron emission tomography imaging.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.  ... 
doi:10.1016/j.compbiomed.2019.02.017 pmid:31054502 pmcid:PMC6531364 fatcat:tcyorm6g3ff6dg7ty2ubtqorjq

Clustered Compressed Sensing in fMRI Data Analysis Using a Bayesian Framework

Solomon Tesfamicael
2014 International Journal of Information and Electronics Engineering  
sparse prior and/ or to the analysis without considering the two priors.  ...  This paper provides a Bayesian method of analyzing functional magnetic resonance imaging (fMRI) data.  ...  Among them are the principal component analysis (PCA) [4] - [6] , independent component analysis (ICA) [7] , [8] and clustering [9] - [18] .  ... 
doi:10.7763/ijiee.2014.v4.412 fatcat:f3nc4e37lngc5e3vkg33w6djpq

A novel denoising algorithm for medical images based on the non‐convex non‐local similar adaptive regularization

Lin Tian, Jiaqing Miao, Xiaobing Zhou, Chao Wang
2021 IET Image Processing  
For a given patch, a non-convex non-local similarity adaptive method is adopted for sparse representation of images.  ...  algorithm to minimize the Euler elastica functional (OSEEF).  ...  Specifically, we use the principal component analysis (PCA) technique as a dictionary-learning technique for setting up a sub-dictionary.  ... 
doi:10.1049/ipr2.12138 fatcat:7vwnvsmy6vdazoowllty76uns4

Performance Evaluation of LPG-PCA Algorithm in Deblurring of CT and MRI Images

R. HariKumar, B. Vinoth Kumar, S. Gowthami
2012 International Journal of Computer Applications  
This paper presents the performance analysis of the LPG-PCA algorithm in deblurring of medical images.  ...  This method involves clustering of data and finding the Sub dictionary of each cluster using LPG-PCA.  ...  CONCLUSION: This paper presented a detailed Performance analysis of local pixel grouping based principal component analysis algorithm in medical images using various image quality measures and results  ... 
doi:10.5120/9777-4355 fatcat:m4ova5c5fnd6pb2a4gxav4zx7u

Hierarchical patch generation for multilevel statistical shape analysis by principal factor analysis decomposition

Mauricio Reyes, Miguel A. González Ballester, Nina Kozic, Ronald M. Summers, Marius George Linguraru, Robert C. Molthen, John B. Weaver
2010 Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging  
Principal factor analysis was used for decomposition of deformation fields obtained from non-rigid registration at different levels, and provided a compact model to study shape variability within the abdomen  ...  Our method further presents the automated hierarchical partitioning of organs into anatomically significant components that represent potentially important constraints for abdominal diagnosis and modeling  ...  Through functional minimization and the tradeoff between sparseness of the deformations and clusters size, the algorithm provides a clusterization of the 2D surface embedded in the 3D space (we emphasizes  ... 
doi:10.1117/12.843253 fatcat:px73kozbhfhafjomrjp6svgdai

ANATOMICAL VARIABILITY OF ORGANS VIA PRINCIPAL FACTOR ANALYSIS FROM THE CONSTRUCTION OF AN ABDOMINAL PROBABILISTIC ATLAS

Mauricio Reyes, Miguel A Gonzalez Ballester, Zhixi Li, Nina Kozic, See Chin, Ronald M Summers, Marius George Linguraru
2009 IEEE International Symposium on Biomedical Imaging  
Additionally, we present a scheme for the study of anatomical variability within the abdomen, including the clusterization of the modes of variation.  ...  The atlas and its dependencies represent a potentially important research tool for abdominal diagnosis, modeling and soft tissue interventions.  ...  Through functional minimization and the tradeoff between sparseness of the deformations and clusters size, the algorithm provides a clusterization of the 2D surface embedded in the 3D space (we emphasize  ... 
pmid:20628477 pmcid:PMC2902185 fatcat:zdemschvxbc75fxwgmm4j6puti
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