Multi-figure anatomical objects for shape statistics

Authors:
Qiong Han;Stephen M. Pizer;Derek Merck;Sarang Joshi;Ja-Yeon Jeong
Affiliations:
Medical Image Display and Analysis Group, University of North Carolina at Chapel Hill, NC;Medical Image Display and Analysis Group, University of North Carolina at Chapel Hill, NC;Medical Image Display and Analysis Group, University of North Carolina at Chapel Hill, NC;Medical Image Display and Analysis Group, University of North Carolina at Chapel Hill, NC;Medical Image Display and Analysis Group, University of North Carolina at Chapel Hill, NC
Venue:
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Year:
2005

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Abstract

Multi-figure m-reps allow us to represent and analyze a complex anatomical object by its parts, by relations among its parts, and by the object itself as a whole entity. This representation also enables us to gather either global or hierarchical statistics from a population of such objects. We propose a framework to train the statistics of multi-figure anatomical objects from real patient data. This training requires fitting multi-figure m-reps to binary characteristic images of training objects. To evaluate the fitting approach, we propose a Monte Carlo method sampling the trained statistics. It shows that our methods generate geometrically proper models that are close to the set of Monte Carlo generated target models and thus can be expected to yield similar statistics to that used for the Monte Carlo generation.