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

作者：Noyel Guillaume^*; Angulo Jesus; Jeulin Dominique; Balvay Daniel; Cuenod Charles Andre

来源：Image Analysis and Stereology, 2015, 34(1): 1-25.

DOI：10.5566/ias.1109

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

We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. In 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 that selects 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.

• 出版日期2015