GPU-based active contour segmentation using gradient vector flow

Authors:
Zhiyu He;Falko Kuester
Affiliations:
Calit2 Center of GRAVITY, University of California, Irvine;Calit2 Center of GRAVITY, University of California, Irvine
Venue:
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Year:
2006

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Abstract

One fundamental step for image-related research is to obtain an accurate segmentation. Among the available techniques, the active contour algorithm has emerged as an efficient approach towards image segmentation. By progressively adjusting a reference curve using combination of external and internal force computed from the image, feature edges can be identified. The Gradient Vector Flow (GVF) is one efficient external force calculation for the active contour and a GPU-centric implementation of the algorithm is presented in this paper. Since the internal SIMD architecture of the GPU enables parallel computing, General Purpose GPU (GPGPU) based processing can be applied to improve the speed of the GVF active contour for large images. Results of our experiments show the potential of GPGPU in the area of image segmentation and the potential of the GPU as a powerful co-processor to traditional CPU computational tasks.