Abstract
In this paper we present a new variational framework in level set form for image segmentation, which incorporates both a prior shape and prior fixed locations of a small number of points. The idea underlying the model is the creation of two energy terms in the energy function for the geodesic active contours. The first energy term is for the shape, the second for the locations of the points In this model, segmentation is achieved through a registration technique, which combines a rigid transformation and a local deformation. The rigid transformation is determined explicitly by using shape information, while the local deformation is determined implicitly by using image gradients and prior locations. We report experimental results on both synthetic and ultrasound images. These results compared with the results obtained by using a previously reported model, which only incorporates a shape prior into the active contours.
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Chen, Y., Guo, W., Huang, F., Wilson, D., Geiser, E.A. (2003). Using Prior Shape and Points in Medical Image Segmentation. In: Rangarajan, A., Figueiredo, M., Zerubia, J. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2003. Lecture Notes in Computer Science, vol 2683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45063-4_19
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DOI: https://doi.org/10.1007/978-3-540-45063-4_19
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