Shape Constraint Strategies: Novel Approaches and Comparative Robustness
Juan J. Cerrolaza, Arantxa Villanueva, Rafael Cabeza · 2011
Active Shape Models are some of the most actively researched model-based segmentation approaches. An accurate estimation of the shape probability distribution is essential to provide the prior knowledge that makes ASMs able to handle the large inherent variability of anatomical structures, differentiating between allowed and invalid instances. Under the typical assumption of normality the subspace of allowed shapes (SAS) is confined within a hyperellipsoid. Although the approximation of the SAS by a hypercube provides computational advantages, this simplification allows the occurrence of highly improbable instances. In addition, a high dependency on the rest of the configuration parameters is observed when the general segmentation algorithm incorporates the hypercube simplification. In this work, a new, efficient hyperelliptical approximation of the SAS based on the Newton-Raphson optimisation method is presented. To perform a detailed comparative study of the effect that four different SAS estimation approaches have on the general segmentation process, a generalisation of the typical two-factor factorial design is used on two different image databases. The results obtained by means of this statistical technique not only reveal the superiority of the new hyperelliptical method in terms of both accuracy and robustness but also provide information of great interest for optimising the segmentation process.