GAMEs: growing and adaptive meshes for fully automatic shape modeling and analysis.
This paper presents a new framework for shape modeling and analysis, rooted in the pattern recognition theory and based on artificial neural networks. Growing and adaptive meshes (GAMEs) are introduced: GAMEs combine the self-organizing networks which grow when require (SONGWR) algorithm and the Kohonen's self-organizing maps (SOMs) in order to build a mesh representation of a given shape and adapt it to instances of similar shapes. The modeling of a surface is seen as an unsupervised clustering problem, and tackled by using SONGWR (topology-learning phase). The point correspondence between point distribution models is granted by adapting the original model to other instances: the adaptation is seen as a classification task and performed accordingly to SOMs (topology-preserving phase). We thoroughly evaluated our method on challenging synthetic datasets, with different levels of noise and shape variations. Finally, we describe its application to the analysis of a challenging medical dataset. Our method proved to be reproducible, robust to noise, and capable of capturing real variations within and between groups of shapes.
Ferrarini
Luca
L
LKEB - Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands. L.Ferrarini@lumc.nl
Olofsen
Hans
H
Palm
Walter M
WM
van Buchem
Mark A
MA
Reiber
Johan H C
JH
Admiraal-Behloul
Faiza
F
D000544:Alzheimer Disease / Q000473:pathology*; D002552:Cerebral Ventricles / Q000033:anatomy & histology* / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008953:Models, Anatomic; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results