Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-40261-6_27
Title: Dense correspondence of skull models by automatic detection of anatomical landmarks
Authors: Zhang, K.
Cheng, Y.
Leow, W.K. 
Keywords: anatomical landmarks
Dense correspondence
skull models
Issue Date: 2013
Citation: Zhang, K.,Cheng, Y.,Leow, W.K. (2013). Dense correspondence of skull models by automatic detection of anatomical landmarks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8047 LNCS (PART 1) : 229-236. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-40261-6_27
Abstract: Determining dense correspondence between 3D skull models is a very important but difficult task due to the complexity of the skulls. Non-rigid registration is at present the predominant approach for dense correspondence. It registers a reference model to a target model and then resamples the target according to the reference. Methods that use manually marked corresponding landmarks are accurate, but manual marking is tedious and potentially error prone. On the other hand, methods that automatically detect correspondence based on local geometric features are sensitive to noise and outliers, which can adversely affect their accuracy. This paper presents an automatic dense correspondence method for skull models that combines the strengths of both approaches. First, anatomical landmarks are automatically and accurately detected to serve as hard constraints for non-rigid registration. They ensure that the correspondence is anatomically consistent and accurate. Second, control points are sampled on the skull surfaces to serve as soft constraints for non-rigid registration. They provide additional local shape constraints for a closer match between the reference and the target. Test results show that, by combining both approaches, our algorithm can achieve more accurate automatic dense correspondence. © 2013 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/78086
ISBN: 9783642402609
ISSN: 03029743
DOI: 10.1007/978-3-642-40261-6_27
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