3D statistical shape modeling of long bones

Yuhui Yang, Anthony Bull, Daniel Rueckert, Adam Hill

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


The aims of this paper are to devise robust methods for the description of the variability in shapes of long bones using 3D statistical shape models (SSMs), and to test these on a dataset of humeri that demonstrate significant variability in shape. 30 primate humeri were CT scanned and manually segmented. SSMs were constructed from a training set of landmarks. The landmarks of the 3D shapes are extracted automatically using marching cubes and point correspondences are automatically obtained via a volumetric non-rigid registration technique using multiresolution B-Spline deformations. The surface registration resulted in no discernible differences between bone shapes, demonstrating the high accuracy of the registration method. An analysis of variations is applied on the shapes based on the model we built. The first mode of variation accounted for 42% of the variation in bone shape. This single component discriminated directly between great apes (including humans) and monkeys.

Original languageEnglish
Title of host publicationBiomedical Image Registration - Third International Workshop, WBIR 2006, Proceedings
PublisherSpringer Verlag
Number of pages9
ISBN (Print)3540356487, 9783540356486
StatePublished - 2006
Externally publishedYes
Event3rd International Workshop on Biomedical Image Registration, WBIR 2006 - Utrecht, Netherlands
Duration: 9 Jul 200611 Jul 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4057 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Workshop on Biomedical Image Registration, WBIR 2006


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