Spectral clustering algorithms for ultrasound image segmentation

Authors:
Neculai Archip;Robert Rohling;Peter Cooperberg;Hamid Tahmasebpour;Simon K. Warfield
Affiliations:
Computational Radiology Laboratory, Harvard Medical School, Departments of Radiology, Brigham and Women’s Hospital, Children's Hospital, Boston, MA;Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada;Department of Radiology, University of British Columbia, Vancouver, BC, Canada;Department of Radiology, University of British Columbia, Vancouver, BC, Canada;Computational Radiology Laboratory, Harvard Medical School, Departments of Radiology, Brigham and Women's Hospital, Children's Hospital, Boston, MA
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2005

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Abstract

Image segmentation algorithms derived from spectral clustering analysis rely on the eigenvectors of the Laplacian of a weighted graph obtained from the image. The NCut criterion was previously used for image segmentation in supervised manner. We derive a new strategy for unsupervised image segmentation. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The extension of the NCut technique to the unsupervised clustering is first described. The novel segmentation algorithm is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. Comparisons with the classical NCut algorithm are also presented. Finally, segmentation results on other types of medical images are shown.