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Proceedings Paper

Spectral embedding based active contour (SEAC): application to breast lesion segmentation on DCE-MRI
Author(s): Shannon C. Agner; Jun Xu; Mark Rosen; Sudha Karthigeyan; Sarah Englander; Anant Madabhushi
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Paper Abstract

Spectral embedding (SE), a graph-based manifold learning method, has previously been shown to be useful in high dimensional data classification. In this work, we present a novel SE based active contour (SEAC) segmentation scheme and demonstrate its applications in lesion segmentation on breast dynamic contrast enhance magnetic resonance imaging (DCE-MRI). In this work, we employ SE on DCE-MRI on a per voxel basis to embed the high dimensional time series intensity vector into a reduced dimensional space, where the reduced embedding space is characterized by the principal eigenvectors. The orthogonal eigenvector-based data representation allows for computation of strong tensor gradients in the spectrally embedded space and also yields improved region statistics that serve as optimal stopping criteria for SEAC. We demonstrate both analytically and empirically that the tensor gradients in the spectrally embedded space are stronger than the corresponding gradients in the original grayscale intensity space. On a total of 50 breast DCE-MRI studies, SEAC yielded a mean absolute difference (MAD) of 3.2±2.1 pixels and mean Dice similarity coefficient (DSC) of 0.74±0.13 compared to manual ground truth segmentation. An active contour in conjunction with fuzzy c-means (FCM+AC), a commonly used segmentation method for breast DCE-MRI, produced a corresponding MAD of 7.2±7.4 pixels and mean DSC of 0.58±0.32. In conjunction with a set of 6 quantitative morphological features automatically extracted from the SEAC derived lesion boundary, a support vector machine (SVM) classifier yielded an area under the curve (AUC) of 0.73, for discriminating between 10 benign and 30 malignant lesions; the corresponding SVM classifier with the FCM+AC derived morphological features yielded an AUC of 0.65.

Paper Details

Date Published: 15 March 2011
PDF: 12 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796305 (15 March 2011); doi: 10.1117/12.878218
Show Author Affiliations
Shannon C. Agner, Rutgers, The State Univ. of New Jersey (United States)
Jun Xu, Rutgers, The State Univ. of New Jersey (United States)
Mark Rosen, Hospital at the Univ. of Pennsylvania (United States)
Sudha Karthigeyan, Rutgers, The State Univ. of New Jersey (United States)
Sarah Englander, Hospital at the Univ. of Pennsylvania (United States)
Anant Madabhushi, Rutgers, The State Univ. of New Jersey (United States)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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