Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Optimal principal component analysis of STEM XEDS spectrum images.

ABSTRACT: STEM XEDS spectrum images can be drastically denoised by application of the principal component analysis (PCA). This paper looks inside the PCA workflow step by step on an example of a complex semiconductor structure consisting of a number of different phases. Typical problems distorting the principal components decomposition are highlighted and solutions for the successful PCA are described. Particular attention is paid to the optimal truncation of principal components in the course of reconstructing denoised data. A novel accurate and robust method, which overperforms the existing truncation methods is suggested for the first time and described in details.

SUBMITTER: Potapov P

PROVIDER: S-EPMC6456488 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Optimal principal component analysis of STEM XEDS spectrum images.

Potapov Pavel P Lubk Axel A

Advanced structural and chemical imaging 20190409 1

STEM XEDS spectrum images can be drastically denoised by application of the principal component analysis (PCA). This paper looks inside the PCA workflow step by step on an example of a complex semiconductor structure consisting of a number of different phases. Typical problems distorting the principal components decomposition are highlighted and solutions for the successful PCA are described. Particular attention is paid to the optimal truncation of principal components in the course of reconstr ...[more]

PMID: 31032174

Similar Datasets

Fast principal component analysis for cryo-electron microscopy images.

Project description:Principal component analysis (PCA) plays an important role in the analysis of cryo-electron microscopy (cryo-EM) images for various tasks such as classification, denoising, compression, and ab initio modeling. We introduce a fast method for estimating a compressed representation of the 2-D covariance matrix of noisy cryo-EM projection images affected by radial point spread functions that enables fast PCA computation. Our method is based on a new algorithm for expanding images in the Fourier-Bessel basis (the harmonics on the disk), which provides a convenient way to handle the effect of the contrast transfer functions. For N images of size L × L, our method has time complexity O(NL3 + L4) and space complexity O(NL2 + L3). In contrast to previous work, these complexities are independent of the number of different contrast transfer functions of the images. We demonstrate our approach on synthetic and experimental data and show acceleration by factors of up to two orders of magnitude.

| S-EPMC10465116 | biostudies-literature

Principal component analysis of dynamic relative displacement fields estimated from MR images.

Project description:Non-destructive measurement of acceleration-induced displacement fields within a closed object is a fundamental challenge. Inferences of how the brain deforms following skull impact have thus relied largely on indirect estimates and course-resolution cadaver studies. We developed a magnetic resonance technique to quantitatively identify the modes of displacement of an accelerating soft object relative to an object enclosing it, and applied it to study acceleration-induced brain deformation in human volunteers. We show that, contrary to the prevailing hypotheses of the field, the dominant mode of interaction between the brain and skull in mild head acceleration is one of sliding arrested by meninges.

| S-EPMC3136495 | biostudies-literature

PRINCIPAL COMPONENT ANALYSIS FOR EXPERIMENTS

Project description:Although principal component analysis is frequently used in multivariate/ analysis, it has disadvantages when applied to experimental or diagnostic data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the set. Second, the method is sensitive to experimental noise and bias between sample groups, since it cannot reflect the design of experiments; rather, it estimates the same weight and independence of all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. The resulting components were scaled to unify their size unit. Also, the principal axes were identified using training data sets and shared among experiments. This training data reflects the design of experiments, and its preparation allows noise to be reduced and group bias to be removed. The effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. Additionally, unknown samples were appropriately classified using pre-arranged axes, and principal axes well reflected the characteristics of groups in the experiments. This SuperSeries is composed of the SubSeries listed below.

2011-08-15 | GSE31375 | GEO

Using the robust principal component analysis algorithm to remove RF spike artifacts from MR images.

Project description:PurposeBrief bursts of RF noise during MR data acquisition ("k-space spikes") cause disruptive image artifacts, manifesting as stripes overlaid on the image. RF noise is often related to hardware problems, including vibrations during gradient-heavy sequences, such as diffusion-weighted imaging. In this study, we present an application of the Robust Principal Component Analysis (RPCA) algorithm to remove spike noise from k-space.MethodsCorrupted k-space matrices were decomposed into their low-rank and sparse components using the RPCA algorithm, such that spikes were contained within the sparse component and artifact-free k-space data remained in the low-rank component. Automated center refilling was applied to keep the peaked central cluster of k-space from misclassification in the sparse component.ResultsThis algorithm was demonstrated to effectively remove k-space spikes from four data types under conditions generating spikes: (i) mouse heart T1 mapping, (ii) mouse heart cine imaging, (iii) human kidney diffusion tensor imaging (DTI) data, and (iv) human brain DTI data. Myocardial T1 values changed by 86.1 ± 171 ms following despiking, and fractional anisotropy values were recovered following despiking of DTI data.ConclusionThe RPCA despiking algorithm will be a valuable postprocessing method for retrospectively removing stripe artifacts without affecting the underlying signal of interest. Magn Reson Med 75:2517-2525, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

| S-EPMC4720596 | biostudies-literature

Search for morphological indicators that predict implantation by principal component analysis using images of blastocyst.

Project description:BackgroundAlthough the current evaluation of human blastocysts is based on the Gardner criteria, there may be other notable parameters. The purpose of our study was to clarify whether the morphology of blastocysts has notable indicators other than the Gardner criteria.MethodsTo find such indicators, we compared blastocysts that showed elevated human chorionic gonadotropin (hCG) levels after transplantation (hCG-positive group; n = 129) and those that did not (hCG-negative group; n = 105) using principal component analysis of pixel brightness of the images.ResultsThe comparison revealed that the hCG-positive group had grainy morphology and the hCG-negative group had non-grainy morphology. Classification of the blastocysts by this indicator did not make a difference in Gardner score. Interestingly, all embryos with ≥20% fragmentation were non-grainy. The visual classification based on this analysis was significantly more accurate than the prediction of implantation using the Gardner score ≥3BB. As graininess can be used in combination with the Gardner score, this indicator will enhance current reproductive technologies.

| S-EPMC9119295 | biostudies-literature

Structured functional principal component analysis.

Project description:Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for high-dimensional data. Methods are used in applications including high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity during sleep.

| S-EPMC4383722 | biostudies-literature

Localized Functional Principal Component Analysis.

Project description:We propose localized functional principal component analysis (LFPCA), looking for orthogonal basis functions with localized support regions that explain most of the variability of a random process. The LFPCA is formulated as a convex optimization problem through a novel Deflated Fantope Localization method and is implemented through an efficient algorithm to obtain the global optimum. We prove that the proposed LFPCA converges to the original FPCA when the tuning parameters are chosen appropriately. Simulation shows that the proposed LFPCA with tuning parameters chosen by cross validation can almost perfectly recover the true eigenfunctions and significantly improve the estimation accuracy when the eigenfunctions are truly supported on some subdomains. In the scenario that the original eigenfunctions are not localized, the proposed LFPCA also serves as a nice tool in finding orthogonal basis functions that balance between interpretability and the capability of explaining variability of the data. The analyses of a country mortality data reveal interesting features that cannot be found by standard FPCA methods.

| S-EPMC4721272 | biostudies-literature

Longitudinal functional principal component analysis.

Project description:We introduce models for the analysis of functional data observed at multiple time points. The dynamic behavior of functional data is decomposed into a time-dependent population average, baseline (or static) subject-specific variability, longitudinal (or dynamic) subject-specific variability, subject-visit-specific variability and measurement error. The model can be viewed as the functional analog of the classical longitudinal mixed effects model where random effects are replaced by random processes. Methods have wide applicability and are computationally feasible for moderate and large data sets. Computational feasibility is assured by using principal component bases for the functional processes. The methodology is motivated by and applied to a diffusion tensor imaging (DTI) study designed to analyze differences and changes in brain connectivity in healthy volunteers and multiple sclerosis (MS) patients. An R implementation is provided.87.

| S-EPMC3131008 | biostudies-literature

MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS.

Project description:The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at two visits. The volume and importance of this data presents enormous challenges for analysis. To address these challenges, we introduce multilevel functional principal component analysis (MFPCA), a novel statistical methodology designed to extract core intra- and inter-subject geometric components of multilevel functional data. Though motivated by the SHHS, the proposed methodology is generally applicable, with potential relevance to many modern scientific studies of hierarchical or longitudinal functional outcomes. Notably, using MFPCA, we identify and quantify associations between EEG activity during sleep and adverse cardiovascular outcomes.

| S-EPMC2835171 | biostudies-literature

Principal component analysis fosr fast and model-free denoising of multi b-value diffusion-weighted MR images.

Project description:Despite the utility of tumour characterisation using quantitative parameter maps from multi-b-value diffusion-weighted MRI (DWI), clinicians often prefer the use of the image with highest diffusion-weighting (b-value), for instance for defining regions of interest (ROIs). However, these images are typically degraded by noise, as they do not utilize the information from the full acquisition. We present a principal component analysis (PCA) approach for model-free denoising of DWI data. PCA-denoising was compared to synthetic MRI, where a diffusion model is fitted for each voxel and a denoised image at a given b-value is generated from the model fit. A quantitative comparison of systematic and random errors was performed on data simulated using several diffusion models (mono-exponential, bi-exponential, stretched-exponential and kurtosis). A qualitative visual comparison was also performed for in vivo images in six healthy volunteers and three pancreatic cancer patients. In simulations, the reduction in random errors from PCA-denoising was substantial (up to 55%) and similar to synthetic MRI (up to 53%). Model-based synthetic MRI denoising resulted in substantial (up to 29% of signal) systematic errors, whereas PCA-denoising was able to denoise without introducing systematic errors (less than 2%). In vivo, the signal-to-noise ratio (SNR) and sharpness of PCA-denoised images were superior to synthetic MRI, resulting in clearer tumour boundaries. In the presence of motion, PCA-denoising did not cause image blurring, unlike image averaging or synthetic MRI. Multi-b-value MRI can be denoised model-free with our PCA-denoising strategy that reduces noise to a level similar to synthetic MRI, but without introducing systematic errors associated with the synthetic MRI method.

| S-EPMC7655121 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data