Image fusion for dynamic contrast enhanced magnetic resonance imaging

Twellmann T, Saalbach A, Gerstung O, Leach MO, Nattkemper TW (2004)
Biomed Eng Online 3(1): 35.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
Twellmann, ThorstenUniBi; Saalbach, Axel; Gerstung, Olaf; Leach, Martin O; Nattkemper, Tim WilhelmUniBi
Abstract / Bemerkung
BACKGROUND: Multivariate imaging techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been shown to provide valuable information for medical diagnosis. Even though these techniques provide new information, integrating and evaluating the much wider range of information is a challenging task for the human observer. This task may be assisted with the use of image fusion algorithms. METHODS: In this paper, image fusion based on Kernel Principal Component Analysis (KPCA) is proposed for the first time. It is demonstrated that a priori knowledge about the data domain can be easily incorporated into the parametrisation of the KPCA, leading to task-oriented visualisations of the multivariate data. The results of the fusion process are compared with those of the well-known and established standard linear Principal Component Analysis (PCA) by means of temporal sequences of 3D MRI volumes from six patients who took part in a breast cancer screening study. RESULTS: The PCA and KPCA algorithms are able to integrate information from a sequence of MRI volumes into informative gray value or colour images. By incorporating a priori knowledge, the fusion process can be automated and optimised in order to visualise suspicious lesions with high contrast to normal tissue. CONCLUSION: Our machine learning based image fusion approach maps the full signal space of a temporal DCE-MRI sequence to a single meaningful visualisation with good tissue/lesion contrast and thus supports the radiologist during manual image evaluation.
Biomed Eng Online
Page URI


Twellmann T, Saalbach A, Gerstung O, Leach MO, Nattkemper TW. Image fusion for dynamic contrast enhanced magnetic resonance imaging. Biomed Eng Online. 2004;3(1): 35.
Twellmann, T., Saalbach, A., Gerstung, O., Leach, M. O., & Nattkemper, T. W. (2004). Image fusion for dynamic contrast enhanced magnetic resonance imaging. Biomed Eng Online, 3(1), 35.
Twellmann, T., Saalbach, A., Gerstung, O., Leach, M. O., and Nattkemper, T. W. (2004). Image fusion for dynamic contrast enhanced magnetic resonance imaging. Biomed Eng Online 3:35.
Twellmann, T., et al., 2004. Image fusion for dynamic contrast enhanced magnetic resonance imaging. Biomed Eng Online, 3(1): 35.
T. Twellmann, et al., “Image fusion for dynamic contrast enhanced magnetic resonance imaging”, Biomed Eng Online, vol. 3, 2004, : 35.
Twellmann, T., Saalbach, A., Gerstung, O., Leach, M.O., Nattkemper, T.W.: Image fusion for dynamic contrast enhanced magnetic resonance imaging. Biomed Eng Online. 3, : 35 (2004).
Twellmann, Thorsten, Saalbach, Axel, Gerstung, Olaf, Leach, Martin O, and Nattkemper, Tim Wilhelm. “Image fusion for dynamic contrast enhanced magnetic resonance imaging”. Biomed Eng Online 3.1 (2004): 35.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
Dieses Objekt ist durch das Urheberrecht und/oder verwandte Schutzrechte geschützt. [...]
Access Level
OA Open Access
Zuletzt Hochgeladen
MD5 Prüfsumme

11 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Color-coded visualization of magnetic resonance imaging multiparametric maps.
Kather JN, Weidner A, Attenberger U, Bukschat Y, Weis CA, Weis M, Schad LR, Zöllner FG., Sci Rep 7(), 2017
PMID: 28112222
A framework for optimal kernel-based manifold embedding of medical image data.
Zimmer VA, Lekadir K, Hoogendoorn C, Frangi AF, Piella G., Comput Med Imaging Graph 41(), 2015
PMID: 25008538
Integration of DCE-MRI and DW-MRI Quantitative Parameters for Breast Lesion Classification.
Fusco R, Sansone M, Filice S, Granata V, Catalano O, Amato DM, Di Bonito M, D'Aiuto M, Capasso I, Rinaldo M, Petrillo A., Biomed Res Int 2015(), 2015
PMID: 26339597
Standardization of radiological evaluation of dynamic contrast enhanced MRI: application in breast cancer diagnosis.
Furman-Haran E, Feinberg MS, Badikhi D, Eyal E, Zehavi T, Degani H., Technol Cancer Res Treat 13(5), 2014
PMID: 24000989
Multidimensional methods for the formulation of biopharmaceuticals and vaccines.
Maddux NR, Joshi SB, Volkin DB, Ralston JP, Middaugh CR., J Pharm Sci 100(10), 2011
PMID: 21647886
Multiclass detection of cells in multicontrast composite images.
Long X, Cleveland WL, Yao YL., Comput Biol Med 40(2), 2010
PMID: 20022596
Principal component analysis of dynamic contrast enhanced MRI in human prostate cancer.
Eyal E, Bloch BN, Rofsky NM, Furman-Haran E, Genega EM, Lenkinski RE, Degani H., Invest Radiol 45(4), 2010
PMID: 20177391
Principal component analysis of breast DCE-MRI adjusted with a model-based method.
Eyal E, Badikhi D, Furman-Haran E, Kelcz F, Kirshenbaum KJ, Degani H., J Magn Reson Imaging 30(5), 2009
PMID: 19856419

31 References

Daten bereitgestellt von Europe PubMed Central.

Challenges to interpretation of breast MRI.
Kinkel K, Hylton NM., J Magn Reson Imaging 13(6), 2001
PMID: 11382939
Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions?
Kuhl CK, Mielcareck P, Klaschik S, Leutner C, Wardelmann E, Gieseke J, Schild HH., Radiology 211(1), 1999
PMID: 10189459
Detection of Suspicious Lesions in Dynamic Contrast-Enhanced MRI Data
Twellmann T, Saalbach A, Müller C, Nattkemper T, Wismüller A., 2004
Classification of signal-time curves from dynamic MR mammography by neural networks.
Lucht RE, Knopp MV, Brix G., Magn Reson Imaging 19(1), 2001
PMID: 11295347
Benign and malignant breast lesions: diagnosis with multiparametric MR imaging.
Jacobs MA, Barker PB, Bluemke DA, Maranto C, Arnold C, Herskovits EH, Bhujwalla Z., Radiology 229(1), 2003
PMID: 14519877
Clinical Testing of High-Spatial-Resolution Parametric Contrast-Enhanced MR Imaging of the Breast
Kelcz F, Furman-Haran E, Grobgeld D, Degani H., 2002
Modeling tracer kinetics in dynamic Gd-DTPA MR imaging.
Tofts PS., J Magn Reson Imaging 7(1), 1997
PMID: 9039598
Image fusion: Issues, techniques and applications
Pohl C, van J., 1994
Multisensor image fusion in remote sensing: concepts, methods and applications
Pohl C, van J., 1998
Mapping Fire Burns and Veetation Regeneration Using Principal Component Analysis
Richards J, Milne A., 1983
Multi-spectral medical image visualization with self-organizing maps
Manduca A., 1994
Neural maps in remote sensing image analysis.
Villmann T, Merenyi E, Hammer B., Neural Netw 16(3-4), 2003
PMID: 12672434
The challenges in spectral image analysis: An introduction and review of ANN approaches
Merenyi E., 1999

Richards J., 1993

Jolliffe I., 1986

Ritter H, Martinetz T, Schulten K., 1992
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Schölkopf B, Smola A, Müller K., 1998
Magnetic resonance imaging screening in women at genetic risk of breast cancer: imaging and analysis protocol for the UK multicentre study. UK MRI Breast Screening Study Advisory Group.
Brown J, Buckley D, Coulthard A, Dixon AK, Dixon JM, Easton DF, Eeles RA, Evans DG, Gilbert FG, Graves M, Hayes C, Jenkins JP, Jones AP, Keevil SF, Leach MO, Liney GP, Moss SM, Padhani AR, Parker GJ, Pointon LJ, Ponder BA, Redpath TW, Sloane JP, Turnbull LW, Walker LG, Warren RM., Magn Reson Imaging 18(7), 2000
PMID: 11027869
Image Fusion for Dynamic Contrast Enhanced Magnet Resoncance Imaging – Auxiliary Material
Color Sequences for Univariate Maps: Theory, Experiments and Principles
Ware C., 1988

Ware C., 2000
Kernel PCA and De-Noising in Feature Spaces
Mika S, Schölkopf B, Smola A, Müller K, Scholz M, Rätsch G., 1999
Fisher Discriminant Analysis with Kernels
Mika S, Rätsch G, Weston J, Schölkopf B, Müller K., 1999
Input space versus feature space in kernel-based methods.
Scholkopf B, Mika S, Burges CC, Knirsch P, Muller KR, Ratsch G, Smola AJ., IEEE Trans Neural Netw 10(5), 1999
PMID: 18252603

Schölkopf B, Smola A, Müller K., 1999
ROC Graphs: Notes and Practical Considerations for Researchers
Fawcett T., 2003
Receiver operating characteristic (ROC) methodology: the state of the art.
Hanley JA., Crit Rev Diagn Imaging 29(3), 1989
PMID: 2667567
A threshold selection method from gray level histograms
Otsu N., 1979

Sonka M, Hlavac V, Boyle R., 1998
LAPACK – Linear Algebra PACKage


Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®


PMID: 15494072
PubMed | Europe PMC

Suchen in

Google Scholar