Nonlinear denoising and analysis of neuroimages with kernel principal component analysis and pre-image estimation

被引:23
|
作者
Rasmussen, Peter Mondrup [1 ,2 ]
Abrahamsen, Trine Julie [1 ]
Madsen, Kristoffer Hougaard [1 ,3 ]
Hansen, Lars Kai [1 ]
机构
[1] Tech Univ Denmark, DTU Informat, Kongens Lyngby, Denmark
[2] Aarhus Univ Hosp, Danish Natl Res Fdn, Ctr Functionally Integrat Neurosci, Aarhus, Denmark
[3] Univ Copenhagen, Hvidovre Hosp, Danish Res Ctr Magnet Resonance, DK-1168 Copenhagen, Denmark
基金
英国医学研究理事会;
关键词
Multivariate analysis; Classification; Decoding; Nonlinear modeling; Kernel PCA; Pre-image estimation; NPAIRS resampling; FMRI DATA; QUANTITATIVE-EVALUATION; PREDICTION; NPAIRS; ACTIVATION; PATTERNS; REMOVAL; MODEL; PCA;
D O I
10.1016/j.neuroimage.2012.01.096
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
We investigate the use of kernel principal component analysis (PCA) and the inverse problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre-image estimation as a means for image denoising as part of the image preprocessing pipeline. Evaluation of the denoising procedure is performed within a data-driven split-half evaluation framework. ii) We introduce manifold navigation for exploration of a nonlinear data manifold, and illustrate how pre-image estimation can be used to generate brain maps in the continuum between experimentally defined brain states/classes. We base these illustrations on two fMRI BOLD data sets - one from a simple finger tapping experiment and the other from an experiment on object recognition in the ventral temporal lobe. (C) 2012 Elsevier Inc. All rights reserved.
引用
收藏
页码:1807 / 1818
页数:12
相关论文
共 50 条
  • [21] Nonlinear process monitoring using kernel principal component analysis
    Lee, JM
    Yoo, CK
    Choi, SW
    Vanrolleghem, PA
    Lee, IB
    CHEMICAL ENGINEERING SCIENCE, 2004, 59 (01) : 223 - 234
  • [22] Kernel principal component analysis for content based image retrieval
    Cha, GH
    ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2005, 3518 : 844 - 849
  • [23] Fault detection and estimation using kernel principal component analysis
    Kallas, Maya
    Mourot, Gilles
    Anani, Kwami
    Ragot, Jose
    Maquin, Didier
    IFAC PAPERSONLINE, 2017, 50 (01): : 1025 - 1030
  • [24] Kernel Entropy Component Analysis Pre-images for Pattern Denoising
    Jenssen, Robert
    Storas, Ola
    IMAGE ANALYSIS, PROCEEDINGS, 2009, 5575 : 626 - 635
  • [25] NONLOCAL MEANS IMAGE DENOISING BASED ON BIDIRECTIONAL PRINCIPAL COMPONENT ANALYSIS
    Chen, Hsin-Hui
    Ding, Jian-Jiun
    2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP), 2015, : 1265 - 1269
  • [26] Image Noise Level Estimation by Principal Component Analysis
    Pyatykh, Stanislav
    Hesser, Juergen
    Zheng, Lei
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (02) : 687 - 699
  • [27] Ensemble Kernel Principal Component Analysis for Improved Nonlinear Process Monitoring
    Li, Nan
    Yang, Yupu
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2015, 54 (01) : 318 - 329
  • [28] Nonlinear reduction of combustion composition space with kernel principal component analysis
    Mirgolbabaei, Hessam
    Echekki, Tarek
    COMBUSTION AND FLAME, 2014, 161 (01) : 118 - 126
  • [29] Block adaptive kernel principal component analysis for nonlinear process monitoring
    Xie, Lei
    Li, Zhe
    Zeng, Jiusun
    Kruger, Uwe
    AICHE JOURNAL, 2016, 62 (12) : 4334 - 4345
  • [30] Nonlinear kernel density principal component analysis with application to climate data
    Seppo Pulkkinen
    Statistics and Computing, 2016, 26 : 471 - 492