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Fully automated segmentation of the pectoralis muscle boundary in breast MR images

 
: Wang, L.; Filippatos, K.; Friman, O.; Hahn, H.K.

:

Summers, R.M. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical imaging 2011. Computer-aided diagnosis. Vol.2 : 15 - 17 February 2011, Lake Buena Vista, [Florida], United States
Bellingham, WA: SPIE, 2011 (Proceedings of SPIE 7963)
ISBN: 978-0-8194-8505-2
ISSN: 1605-7422
Art. 796309
Medical Imaging Symposium <2011, Lake Buena Vista/Fla.>
Englisch
Konferenzbeitrag
Fraunhofer MEVIS ()

Abstract
Dynamic Contrast Enhanced MRI (DCE-MRI) of the breast is emerging as a novel tool for early tumor detection and diagnosis. The segmentation of the structures in breast DCE-MR images, such as the nipple, the breast-air boundary and the pectoralis muscle, serves as a fundamental step for further computer assisted diagnosis (CAD) applications, e.g. breast density analysis. Moreover, the previous clinical studies show that the distance between the posterior breast lesions and the pectoralis muscle can be used to assess the extent of the disease. To enable automatic quantification of the distance from a breast tumor to the pectoralis muscle, a precise delineation of the pectoralis muscle boundary is required. We present a fully automatic segmentation method based on the second derivative information represented by the Hessian matrix. The voxels proximal to the pectoralis muscle boundary exhibit roughly the same Eigen value patterns as a sheet-like object in 3D, which can be enhanced and segmented by a Hessian-based sheetness filter. A vector-based connected component filter is then utilized such that only the pectoralis muscle is preserved by extracting the largest connected component. The proposed method was evaluated quantitatively with a test data set which includes 30 breast MR images by measuring the average distances between the segmented boundary and the annotated surfaces in two ground truth sets, and the statistics showed that the mean distance was 1.434 mm with the standard deviation of 0.4661 mm, which shows great potential for integration of the approach in the clinical routine.

: http://publica.fraunhofer.de/dokumente/N-173269.html