Abstract
Segmentation of anatomical structures via minimal surface extraction using gradient-based metrics is a popular approach, but exhibits some limits in the case of weak or missing contour information. We propose a new framework to define metrics, robust to missing image information. Given an object of interest we combine gray-level information and knowledge about the spatial organization of cerebral structures, into a fuzzy set which is guaranteed to include the object’s boundaries. From this set we derive a metric which is used in a minimal surface segmentation framework. We show how this metric leads to improved segmentation of subcortical gray matter structures. Quantitative results on the segmentation of the caudate nucleus in T1 MRI are reported on 18 normal subjects and 6 pathological cases.
Index terms:
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minimal surface segmentation
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level sets
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spatial relations
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fuzzy knowledge representation
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Nempont, O., Atif, J., Angelini, E., Bloch, I. (2007). Combining Radiometric and Spatial Structural Information in a New Metric for Minimal Surface Segmentation. In: Karssemeijer, N., Lelieveldt, B. (eds) Information Processing in Medical Imaging. IPMI 2007. Lecture Notes in Computer Science, vol 4584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73273-0_24
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DOI: https://doi.org/10.1007/978-3-540-73273-0_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73272-3
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