Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/76911
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Type: Conference paper
Title: A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI
Author: Wels, M.
Carneiro, G.
Aplas, A.
Huber, M.
Hornegger, J.
Comaniciu, D.
Citation: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008: 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I / Dimitris Metaxas, Leon Axel, Gabor Fichtinger, Gábor Székely (eds.): pp. 67-75
Publisher: Springer-Verlag Berlin
Publisher Place: Heidelberg
Issue Date: 2008
Series/Report no.: Lecture Notes in Computer Science; 5241
ISBN: 354085987X
9783540859871
ISSN: 0302-9743
1611-3349
Conference Name: International Conference on Medical Image Computing and Computer-Assisted Intervention (11th : 2008 : New York, NY)
Statement of
Responsibility: 
Michael Wels, Gustavo Carneiro, Alexander Aplas, Martin Huber, Joachim Hornegger and Dorin Comaniciu
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.
Rights: © Springer-Verlag Berlin Heidelberg 2008
RMID: 0020114351
DOI: 10.1007/978-3-540-85988-8_9
Appears in Collections:Computer Science publications

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