[en] This work presents development of statistical shape models based on robust and rigid-groupwise registration followed by pointset non-rigid registration. The main advantages of the pipeline include automation in that the method does not rely on manual landmarks or a regionalization step; there is no bias in the choice of reference during the correspondence steps and the use of the Probabilistic Principal Component Analysis framework increases the domain of the shape variability. A comparison between the widely used Expectation Maximization- Iterative Closest Point algorithm and a recently reported groupwise method on publicly available data (hippocampus) using the well-known criteria of generality, specificity and compactness is also presented. The proposed method gives similar values but the curves of generality and specificity are superior to those of the other two methods. Finally the method is applied to the human scapula, which is a known difficult structure, and the human humerus.
Disciplines :
Human health sciences: Multidisciplinary, general & others
Author, co-author :
Mutsvangwa, Tinashe; University of Cape Town > Biomedical Engineering Division
Burdin, Valérie; Telecom Bretagne > LaTIM
Schwartz, Cédric ; Université de Liège - ULiège > Département des sciences de la motricité > Kinésithérapie générale et réadaptation
Roux, Christian; Ecole des mines de Saint-Etienne > Centre Ingénierie et Santé
Language :
English
Title :
An automated statistical shape model developmental pipeline: application to the human scapula and humerus
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