Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies
This work addresses tumor detection in medical imaging for angiogenesis studies, presenting an incremental improvement in segmentation techniques.
The authors tackled tumor detection in DCE-MRI images for small animals by developing a multivariate segmentation method combining dimensionality reduction, noise filtering, and stochastic watershed, with results aligning with medical diagnoses.
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.