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

Shannon C. Agner, Jun Xu, Mark Rosen, Sudha Karthigeyan, Sarah Englander, Anant Madabhushi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


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.

Original languageEnglish
Title of host publicationMedical Imaging 2011
Subtitle of host publicationComputer-Aided Diagnosis
StatePublished - 2011
EventMedical Imaging 2011: Computer-Aided Diagnosis - Lake Buena Vista, FL, United States
Duration: Feb 15 2011Feb 17 2011

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2011: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityLake Buena Vista, FL


  • Active contour
  • breast cancer
  • classification
  • magnetic resonance imaging
  • manifold learning
  • segmentation
  • spectral embedding


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