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Proceedings Paper

AdaBoost classification for model-based segmentation of the outer wall of the common carotid artery in CTA
Author(s): D. Vukadinovic; T. van Walsum; R. Manniesing; A. van der Lugt; T. T. de Weert; W. J. Niessen
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Paper Abstract

A novel 2D slice based automatic method for model based segmentation of the outer vessel wall of the common carotid artery in CTA data set is introduced. The method utilizes a lumen segmentation and AdaBoost, a fast and robust machine learning algorithm, to initially classify (mark) regions outside and inside the vessel wall using the distance from the lumen and intensity profiles sampled radially from the gravity center of the lumen. A similar method using the distance from the lumen and the image intensity as features is used to classify calcium regions. Subsequently, an ellipse shaped deformable model is fitted to the classification result. The method has achieved smaller detection error than the inter observer variability, and the method is robust against variation of the training data sets.

Paper Details

Date Published: 11 March 2008
PDF: 8 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691418 (11 March 2008); doi: 10.1117/12.770232
Show Author Affiliations
D. Vukadinovic, Univ. Medical Ctr. Rotterdam (Netherlands)
T. van Walsum, Univ. Medical Ctr. Rotterdam (Netherlands)
R. Manniesing, Univ. Medical Ctr. Rotterdam (Netherlands)
A. van der Lugt, Univ. Medical Ctr. Rotterdam (Netherlands)
T. T. de Weert, Univ. Medical Ctr. Rotterdam (Netherlands)
W. J. Niessen, Univ. Medical Ctr. Rotterdam (Netherlands)

Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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