Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive multiresolution mesh editing
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Hierarchical B-spline refinement
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
The Use of Active Shape Models for Locating Structures in Medical Images
IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
Tamed Snake: A Particle System for Robust Semi-automatic Segmentation
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
GA Techniques Applied to Contour Search in Images of Bovine Livestock
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Estimating the Confidence of Statistical Model Based Shape Prediction
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Subject-specific cardiac segmentation based on reinforcement learning with shape instantiation
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
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The initialisation of segmentation methods aiming at the localisation of biological structures in medical imagery is frequently regarded as a given precondition. In practice, however, initialisation is usually performed manually or by some heuristic preprocessing steps. Moreover, the same framework is often employed to recover from imperfect results of the subsequent segmentation. Therefore, it is of crucial importance for everyday application to have a simple and effective initialisation method at one's disposal. This paper proposes a new model-based framework to synthesise sound initialisations by calculating the most probable shape given a minimal set of statistical landmarks and the applied shape model. Shape information coded by particular points is first iteratively removed from a statistical shape description that is based on the principal component analysis of a collection of shape instances. By using the inverse of the resulting operation, it is subsequently possible to construct initial outlines with minimal effort. The whole framework is demonstrated by means of a shape database consisting of a set of corpus callosum instances. Furthermore, both manual and fully automatic initialisation with the proposed approach is evaluated. The obtained results validate its suitability as a preprocessing step for semi-automatic as well as fully automatic segmentation. And last but not least, the iterative construction of increasingly point-invariant shape statistics provides a deeper insight into the nature of the shape under investigation.