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True-false lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative-discriminative model matching

Noah Lee; Huseyin Tek; Andrew F. Laine

Title:
True-false lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative-discriminative model matching
Author(s):
Lee, Noah
Tek, Huseyin
Laine, Andrew F.
Date:
Type:
Articles
Department:
Biomedical Engineering
Permanent URL:
Book/Journal Title:
Medical Imaging 2008: Computer-aided Diagnosis: 19-21 February 2008, San Diego, California, USA ; Proceedings of SPIE, vol. 6915
Publisher:
SPIE
Abstract:
Computer aided diagnosis in the medical image domain requires sophisticated probabilistic models to formulate quantitative behavior in image space. In the diagnostic process detailed knowledge of model performance with respect to accuracy, variability, and uncertainty is crucial. This challenge has lead to the fusion of two successful learning schools namely generative and discriminative learning. In this paper, we propose a generative-discriminative learning approach to predict object boundaries in medical image datasets. In our approach, we perform probabilistic model matching of both modeling domains to fuse into the prediction step appearance and structural information of the object of interest while exploiting the strength of both learning paradigms. In particular, we apply our method to the task of true-false lumen segmentation of aortic dissections an acute disease that requires automated quantification for assisted medical diagnosis. We report empirical results for true-false lumen discrimination of aortic dissection segmentation showing superior behavior of the hybrid generative-discriminative approach over their non hybrid generative counterpart.
Subject(s):
Biomedical engineering
Publisher DOI:
10.1117/12.770610
Item views:
310
Metadata:
text | xml

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