Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies

TL;DR

This paper introduces a multivariate mathematical morphology framework for analyzing DCE-MRI images in angiogenesis studies, focusing on noise reduction and spatio-temporal tumor segmentation.

Contribution

It presents a novel multivariate segmentation method combining dimensionality reduction, noise filtering, and stochastic watershed for improved tumor detection in DCE-MRI.

Findings

Effective noise reduction preserving contours

Accurate spatio-temporal tumor segmentation

Results align with medical diagnoses

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. 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.