MULTIVARIATE MATHEMATICAL MORPHOLOGY FOR DCE-MRI IMAGE ANALYSIS IN ANGIOGENESIS STUDIES

Authors

  • Guillaume Noyel MINES ParisTech Centre de Morphologie Mathématique Mathématiques et Systèmes 35 rue Saint Honoré 77305 Fontainebleau France http://orcid.org/0000-0002-7374-548X
  • Jesus Angulo MINES ParisTech Centre de Morphologie Mathématique Mathématiques et Systèmes 35 rue Saint Honoré 77305 Fontainebleau France
  • Dominique Jeulin MINES ParisTech Centre de Morphologie Mathématique Mathématiques et Systèmes 35 rue Saint Honoré 77305 Fontainebleau France
  • Daniel Balvay LRI-PARCC U970 Paris - Descartes University APHP, HEGP, Service de Radiologie Paris France
  • Charles-André Cuenod LRI-PARCC U970 Paris - Descartes University APHP, HEGP, Service de Radiologie Paris France

DOI:

https://doi.org/10.5566/ias.1109

Keywords:

classification, DCE-MRI series, multivariate mathematical morphology, segmentation, stochastic watershed, tumours

Abstract

We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. To perform this approach, we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is performed in a special way to select factorial axes of Factor Correspondence Analysis in order to preserves contours. Then a spatio-temporal approach based on stochastic watershed is used to segment tumours. The results obtained are in accordance with the diagnosis of the medical doctors.

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Published

2014-05-30

Issue

Section

Original Research Paper

How to Cite

Noyel, G., Angulo, J., Jeulin, D., Balvay, D., & Cuenod, C.-A. (2014). MULTIVARIATE MATHEMATICAL MORPHOLOGY FOR DCE-MRI IMAGE ANALYSIS IN ANGIOGENESIS STUDIES. Image Analysis and Stereology, 34(1), 1-25. https://doi.org/10.5566/ias.1109

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