Abstract
This paper proposes an approach based on level sets to segment brain tumors from CT images. Combining edge information with region information dynamically, the novel method introduces a new energy function model, which will make the initial contour evolve towards the desirable boundary while not leak at weak edge positions. In addition, re-initialization of the evolving level set function is avoided by introducing a new simple regularization term, which can eliminate radical changes of level set function(LSF) far away from the contour, and make the LSF prone to be a signed distance function around the contour as well. Experimental results demonstrate that the proposed method performs well on CT images, and can segment brain tumors exactly.
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Wei, Z., Zhang, C., Yang, X., Zhang, X. (2012). Segmentation of Brain Tumors in CT Images Using Level Sets. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33179-4_3
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DOI: https://doi.org/10.1007/978-3-642-33179-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33178-7
Online ISBN: 978-3-642-33179-4
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