Globally Optimal Geodesic Active Contours

Authors:
Ben Appleton;Hugues Talbot
Affiliations:
Intelligent Real-Time Imaging and Sensing Group, ITEE, The University of Queensland, Brisbane, Australia 4072;CSIRO Mathematical and Information Sciences, North Ryde, Australia 1670
Venue:
Journal of Mathematical Imaging and Vision
Year:
2005

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Abstract

An approach to optimal object segmentation in the geodesic active contour framework is presented with application to automated image segmentation. The new segmentation scheme seeks the geodesic active contour of globally minimal energy under the sole restriction that it contains a specified internal point pint. This internal point selects the object of interest and may be used as the only input parameter to yield a highly automated segmentation scheme. The image to be segmented is represented as a Riemannian space S with an associated metric induced by the image. The metric is an isotropic and decreasing function of the local image gradient at each point in the image, encoding the local homogeneity of image features. Optimal segmentations are then the closed geodesics which partition the object from the background with minimal similarity across the partitioning. An efficient algorithm is presented for the computation of globally optimal segmentations and applied to cell microscopy, x-ray, magnetic resonance and cDNA microarray images.