Application of principal component analysis to multispectral imaging data for evaluation of pigmented skin lesions

2013 ◽  
Author(s):  
Dainis Jakovels ◽  
Ilze Lihacova ◽  
Ilona Kuzmina ◽  
Janis Spigulis
Keyword(s):  
Principal Component Analysis ◽  
Multispectral Imaging ◽  
Principal Component ◽  
Component Analysis ◽  
Skin Lesions ◽  
Imaging Data ◽  
Pigmented Skin Lesions

scholarly journals EP-2029 Principal component analysis for quantitative and robust analysis of dynamic PET/MR imaging data

2019 ◽  
Vol 133 ◽  
pp. S1113-S1114
Author(s):  
R. Winter ◽  
S. Leibfarth ◽  
S. Boeke ◽  
P. Mena-Romano ◽  
M. Krueger ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Mr Imaging ◽  
Principal Component ◽  
Component Analysis ◽  
Dynamic Pet ◽  
Imaging Data ◽  
Robust Analysis

scholarly journals Semiparametric Partial Common Principal Component Analysis for Covariance Matrices

2019 ◽  
Author(s):  
Bingkai Wang ◽  
Xi Luo ◽  
Yi Zhao ◽  
Brian Caffo
Keyword(s):  
Principal Component Analysis ◽  
Motor Task ◽  
Principal Component ◽  
Component Analysis ◽  
Testing Procedure ◽  
Covariance Matrices ◽  
Simulation Studies ◽  
Imaging Data ◽  
Asymptotic Results ◽  
Data Set

SummaryWe consider the problem of jointly modeling multiple covariance matrices by partial common principal component analysis (PCPCA), which assumes a proportion of eigenvectors to be shared across covariance matrices and the rest to be individual-specific. This paper proposes consistent estimators of the shared eigenvectors in PCPCA as the number of matrices or the number of samples to estimate each matrix goes to infinity. We prove such asymptotic results without making any assumptions on the ranks of eigenvalues that are associated with the shared eigenvectors. When the number of samples goes to infinity, our results do not require the data to be Gaussian distributed. Furthermore, this paper introduces a sequential testing procedure to identify the number of shared eigenvectors in PCPCA. In simulation studies, our method shows higher accuracy in estimating the shared eigenvectors than competing methods. Applied to a motor-task functional magnetic resonance imaging data set, our estimator identifies meaningful brain networks that are consistent with current scientific understandings of motor networks during a motor paradigm.

Fast model-based multispectral imaging using nonnegative principal component analysis

2012 ◽  
Vol 37 (11) ◽  
pp. 1937 ◽  
Author(s):  
Moon-Hyun Lee ◽  
Hanhoon Park ◽  
In Ryu ◽  
Jong-Il Park
Keyword(s):  
Principal Component Analysis ◽  
Multispectral Imaging ◽  
Principal Component ◽  
Component Analysis ◽  
Model Based
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