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Proceedings Article

Spectral embedding based active contour (SEAC): application to breast lesion segmentation on DCE-MRI

[+] Author Affiliations
Shannon C. Agner, Jun Xu, Sudha Karthigeyan, Anant Madabhushi

Rutgers, The State Univ. of New Jersey (USA)

Mark Rosen, Sarah Englander

Hospital at the Univ. of Pennsylvania (USA)

Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796305 (March 15, 2011); doi:10.1117/12.878218
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From Conference Volume 7963

  • Medical Imaging 2011: Computer-Aided Diagnosis
  • Ronald M. Summers; Bram van Ginneken
  • Lake Buena Vista, Florida | February 12, 2011

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.

© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Shannon C. Agner ; Jun Xu ; Mark Rosen ; Sudha Karthigeyan ; Sarah Englander, et al.
"Spectral embedding based active contour (SEAC): application to breast lesion segmentation on DCE-MRI", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796305 (March 15, 2011); doi:10.1117/12.878218; http://dx.doi.org/10.1117/12.878218


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