Abstract
We present a methodology for estimating the probability of multi-object anatomic complexes that reflects both the individual objects’ variability and the variability of the inter-relationships between objects. The method is based on m-reps and the idea of augmenting medial atoms from one object’s m-rep to the set of atoms of an object being described. We describe the training of these probabilities, and we present an example of calculating the statistics of the bladder, prostate, rectum complex in the male pelvis. Via examples from the real world and from Monte-Carlo simulation, we show that this means of representing multi-object statistics yields samples that are nearly geometrically proper and means and principal modes of variations that are intuitively reasonable.
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Pizer, S.M., Jeong, JY., Lu, C., Muller, K., Joshi, S. (2005). Estimating the Statistics of Multi-object Anatomic Geometry Using Inter-object Relationships. In: Fogh Olsen, O., Florack, L., Kuijper, A. (eds) Deep Structure, Singularities, and Computer Vision. DSSCV 2005. Lecture Notes in Computer Science, vol 3753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11577812_6
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DOI: https://doi.org/10.1007/11577812_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29836-6
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