Abstract
Using snakes to segment images has proven to be a powerful tool in many different applications. The snake is usually propagated by minimizing an energy function. The standard way of updating the snake from the energy function is time consuming. This paper presents a fast snake evolution algorithm, based on a more efficient numeric scheme for updating the snake. Instead of inverting a matrix derived from approximating derivatives in a sampled snake, an analytical expression is obtained. The expression takes the form of a convolution with a filter given by an explicit formula. The filter function can then be sampled and used to propagate snakes in a fast and straightforward manner. The proposed method is generally applicable to snakes and is here used for propagating snakes in a gradient vector flow field. Experiments are carried out on images of histopathological tissue sections and the results are very promising.
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© 2003 Springer-Verlag Berlin Heidelberg
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Karlsson, A., Stråhlén, K., Heyden, A. (2003). A Fast Snake Segmentation Method Applied to Histopathological Sections. 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_17
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DOI: https://doi.org/10.1007/978-3-540-45063-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40498-9
Online ISBN: 978-3-540-45063-4
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