Statistical Shape Analysis of the Human Ear Canal with Application to In-the-Ear Hearing Aid Design

This thesis is about the statistical shape analysis of the human ear canal with application to the mechanical design of in-the-ear hearing aids. Initially, it is described how a statistical shape model of the human ear canal is built based on a training set of laser-scanned ear impressions. A thin plate spline based approach creates a dense correspondence between the shapes in training set. In addition, a new flexible, non-rigid registration framework is proposed and used to optimise the correspondence field. The framework is based on Markov Random Field regularisation and is motivated by prior work on image restoration. It is shown how the method significantly improves the shape model. In the second part of the thesis, the shape model is used in software tools that mimic the skills of the expert hearing aid makers. The first result is that it is possible to learn an algorithm to cut an ear canal in order to produce an optimal in-the-ear hearing aid. Secondly, a framework for component placement using a coupling of stochastic optimisation and the results from the shape model is proposed. It is successfully, used to place the so-called faceplate with associated component on in-the-ear hearing aids. In addition, the idea of one-size-fits-most shells is explored.