Abstract
In this paper, a novel curve evolution strategy driven by boundary statistics for the segmentation of medical images is proposed and realized under the Level Set framework. It has a speed term similar to that of the Chan-Vese’s method [1] for bimodal pictures, but is driven by boundary statistics (the statistics of intensity in an observing window) instead of the global statistics. In the case of multimodal pictures, the target’s shape can, therefore, be more easily recovered. Here, we present methods for shape prediction based on the signed distance functions and extension field constructed from the boundary statistics. Employing the above techniques, our algorithm can adaptively handle both the sharp and smooth edges of the target, and its efficiency is demonstrated in the contour tracking of medical images.
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Li, Y., Tang, Q. (2004). A Level Set Algorithm for Contour Tracking in Medical Images. In: Yang, GZ., Jiang, TZ. (eds) Medical Imaging and Augmented Reality. MIAR 2004. Lecture Notes in Computer Science, vol 3150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28626-4_17
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DOI: https://doi.org/10.1007/978-3-540-28626-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22877-6
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