A Discriminative Model-Constrained Graph Cuts Approach to Fully Automated Pediatric Brain Tumor Segmentation in 3-D MRI

In this paper we present a fully automated approach to the segmentation of pediatric brain tumors in multi-spectral 3-D magnetic resonance images. It is a top-down segmentation approach based on a Markov random field (MRF) model that combines probabilistic boosting trees (PBT) and lower-level segmentation via graph cuts. The PBT algorithm provides a strong discriminative observation model that classifies tumor appearance while a spatial prior takes into account the pair-wise homogeneity in terms of classification labels and multi-spectral voxel intensities. The discriminative model relies not only on observed local intensities but also on surrounding context for detecting candidate regions for pathology. A mathematically sound formulation for integrating the two approaches into a unified statistical framework is given. The proposed method is applied to the challenging task of detection and delineation of pediatric brain tumors. This segmentation task is characterized by a high non-unif...

• Type: Conference
• Year: 2008
• Where: MICCAI
• Authors: Michael Wels, Gustavo Carneiro, Alexander Aplas, Martin Huber, Joachim Hornegger, Dorin Comaniciu
• Keywords: Brain Tumor Segmentation, Medical Imaging, MICCAI 2008, Multi-spectral Voxel Intensities, Pediatric Brain Tumors