Publication year
2012Source
Lecture Notes in Computer Science, 15, Pt 2, (2012), pp. 371-378ISSN
Publication type
Article / Letter to editor
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Organization
Radiology
Data Science
Journal title
Lecture Notes in Computer Science
Volume
vol. 15
Issue
iss. Pt 2
Page start
p. 371
Page end
p. 378
Subject
Data Science; ONCOL 5: Aetiology, screening and detection; Medical Imaging - Radboud University Medical CenterAbstract
Pectoral muscle segmentation is an important step in automatic breast image analysis methods and crucial for multi-modal image registration. In breast MRI, accurate delineation of the pectoral is important for volumetric breast density estimation and for pharmacokinetic analysis of dynamic contrast enhancement. In this paper we propose and study the performance of atlas-based segmentation methods evaluating two fully automatic breast MRI dedicated strategies on a set of 27 manually segmented MR volumes. One uses a probabilistic model and the other is a multi-atlas registration based approach. The multi-atlas approach performed slightly better, with an average Dice coefficient (DSC) of 0.74, while with the much faster probabilistic method a DSC of 0.72 was obtained.
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- Academic publications [246216]
- Faculty of Medical Sciences [93266]
- Faculty of Science [37928]
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