A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI
Wels M, Carneiro G, Aplas A, Huber M, Hornegger J, Comaniciu D (2008)
Publication Type: Authored book, Volume of book series
Publication year: 2008
Journal
Original Authors: Wels M., Carneiro G., Aplas A., Huber M., Hornegger J., Comaniciu D.
Publisher: Springer-verlag
Series: Lecture Notes on Computer Science
City/Town: Berlin
Book Volume: 5241
Pages Range: 67-75
Event location: New York, NY
Journal Issue: null
ISBN: 3-540-44707-5
DOI: 10.1007/978-3-540-85988-8_9
Abstract
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-uniformity of both the pathology and the surrounding non-pathologic brain tissue. A quantitative evaluation illustrates the robustness of the proposed method. Despite dealing with more complicated cases of pediatric brain tumors the results obtained are mostly better than those reported for current state-of-the-art approaches to 3-D MR brain tumor segmentation in adult patients. The entire processing of one multi-spectral data set does not require any user interaction, and takes less time than previously proposed methods. © 2008 Springer-Verlag Berlin Heidelberg.
Authors with CRIS profile
Involved external institutions
University of South Australia
Australia (AU)
Siemens AG, Sector Corporate Technology
Germany (DE)
Siemens Corporate Research
United States (USA) (US)
Australia (AU)
Siemens AG, Sector Corporate Technology
Germany (DE)
Siemens Corporate Research
United States (USA) (US)
How to cite
APA:
Wels, M., Carneiro, G., Aplas, A., Huber, M., Hornegger, J., & Comaniciu, D. (2008). A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI. Berlin: Springer-verlag.
MLA:
Wels, Michael, et al. A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI. Berlin: Springer-verlag, 2008.
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