Multivariate image analysis in biomedicine

Nattkemper TW (2004)

No fulltext has been uploaded. References only!
Journal Article | Review | Published | English

No fulltext has been uploaded

Abstract / Notes
In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projets and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis. (C) 2004 Elsevier Inc. All rights reserved.
Publishing Year

Cite this

Nattkemper TW. Multivariate image analysis in biomedicine. JOURNAL OF BIOMEDICAL INFORMATICS. 2004;37(5):380-391.
Nattkemper, T. W. (2004). Multivariate image analysis in biomedicine. JOURNAL OF BIOMEDICAL INFORMATICS, 37(5), 380-391. doi:10.1016/j.jbi.2004.07.010
Nattkemper, T. W. (2004). Multivariate image analysis in biomedicine. JOURNAL OF BIOMEDICAL INFORMATICS 37, 380-391.
Nattkemper, T.W., 2004. Multivariate image analysis in biomedicine. JOURNAL OF BIOMEDICAL INFORMATICS, 37(5), p 380-391.
T.W. Nattkemper, “Multivariate image analysis in biomedicine”, JOURNAL OF BIOMEDICAL INFORMATICS, vol. 37, 2004, pp. 380-391.
Nattkemper, T.W.: Multivariate image analysis in biomedicine. JOURNAL OF BIOMEDICAL INFORMATICS. 37, 380-391 (2004).
Nattkemper, Tim Wilhelm. “Multivariate image analysis in biomedicine”. JOURNAL OF BIOMEDICAL INFORMATICS 37.5 (2004): 380-391.
This data publication is cited in the following publications:
This publication cites the following data publications:

6 Citations in Europe PMC

Data provided by Europe PubMed Central.

Multiclass detection of cells in multicontrast composite images.
Long X, Cleveland WL, Yao YL., Comput Biol Med 40(2), 2010
PMID: 20022596
Linked Exploratory Visualizations for Uncertain MR Spectroscopy Data.
Feng D, Kwock L, Lee Y, Taylor RM., Vis Data Anal 7530(), 2010
PMID: 21152337
Evaluation of Glyph-based Multivariate Scalar Volume Visualization Techniques.
Feng D, Lee Y, Kwock L, Taylor RM., Proc APGV 2009(), 2009
PMID: 21559056
Fully automated classification of HARDI in vivo data using a support vector machine.
Schnell S, Saur D, Kreher BW, Hennig J, Burkhardt H, Kiselev VG., Neuroimage 46(3), 2009
PMID: 19285561
The neurological basis of occupation.
Gutman SA, Schindler VP., Occup Ther Int 14(2), 2007
PMID: 17623380

98 References

Data provided by Europe PubMed Central.



Cartograms for visualizing human geography
Dorling, 1994

Tactical audio and acoustic rendering in biomedical applications
Jovanov, IEEE Trans Inform Technol Biomed 3(2), 1999


Dynamic representation of multivariate time series data
Mezrich, J. Am Stat Assoc 79(385), 1984
Computer–human interface issues in the design of an intelligent workstation for scientific visualization
Williams, SIGCHI Bull 21(4), 1990
Quantitation of prostate-specific acid phosphatase in prostate cancer: reproducibility and correlation with subjective grade.
Zhou R, Hammond EH, Sause WT, Rubin P, Emami B, Pilepich MV, Asbell SD, Parker DL., Mod. Pathol. 7(4), 1994
PMID: 7520585
An introduction to variable and feature selection
Guyon, JMLR: Special Issue on Variable and Feature Selection 3(), 2003

Vapnik, 1995
Extracting tree-structured representations of trained networks
Craven, 1996
Discriminative direction for kernel classifiers
Golland, 2002
Computer-assisted analysis of epiluminescence microscopy images of pigmented skin lesions.
Debeir O, Decaestecker C, Pasteels JL, Salmon I, Kiss R, Van Ham P., Cytometry 37(4), 1999
PMID: 10547610
Leucocyte activation markers in clinical practice.
Viedma Contreras JA., Clin. Chem. Lab. Med. 37(6), 1999
PMID: 10475068
Human vs. machine: Evaluation of fluorescence micrographs
Nattkemper, Comput Biol Med 33(1), 2002

Perceptualization of biomedical data. An experimental environment for visualization and sonification of brain electrical activity.
Jovanov E, Starcevic D, Radivojevic V, Samardzic A, Simeunovic V., IEEE Eng Med Biol Mag 18(1), 1999
PMID: 9934600


0 Marked Publications

Open Data PUB

Web of Science

View record in Web of Science®


PMID: 15488751
PubMed | Europe PMC

Search this title in

Google Scholar