Building and Testing a Statistical Shape Model of the Human Ear Canal
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Customized Design of Hearing Aids Using Statistical Shape Learning
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Automatic detection of anatomical features on 3D ear impressions for canonical representation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
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We propose a novel framework for the personalized design of organic shapes that are constrained to exhibit conformity with the underlying anatomy. Such constrained design is significant for several applications such as the design of implants and prosthetics, which need to be adapted to the anatomy of a patient. In such applications, vaguely defined work instructions are usually employed by expert designers to carry out a sequence of surface modification operations using interactive CAD tools. Our approach involves the abstraction of the work instructions and the expert knowledge into feature dependent machine interpretable rules in a Knowledge Base. Robustly identified canonical set of anatomical features are then employed to determine concrete surface shaping operations by a Smart Shape Modeler. These operations are eventually performed sequentially to adapt a surface to a target shape. The versatility of our approach lies in a priori defining an entire design workflow through a scripting language, thereby yielding a high degree of automation that is completely flexible and customizable via scriptable rules. Consequently, it eliminates tediousmanual intervention and offers desirable precision and reproducibility. We validate this framework with a practical application - automatic modeling of shells in hearing aid (HA) manufacturing (HAM).