Multivariate image analysis in biomedicine

Nattkemper TW (2004)

Journal Article | Published | English

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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.
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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.
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.
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