Mr. Ibraheem Al-Dhamari Profile

Ibraheem Al-Dhamari

at Koblenz and Landau Univ

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Proceedings Article | 24 February 2017 Presentation + Paper

ACIR: automatic cochlea image registration

Ibraheem Al-Dhamari, Sabine Bauer, Dietrich Paulus, Friedrich Lissek, Roland Jacob

Proceedings Volume 10133, 1013310 (2017) https://doi.org/10.1117/12.2254396

KEYWORDS: Multimodal imaging, Ear, Image registration, Medical imaging, 3D image processing, Computed tomography, Surgery, Head, Brain-machine interfaces, Stochastic processes, Image fusion

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Efficient Cochlear Implant (CI) surgery requires prior knowledge of the cochlea’s size and its characteristics. This information helps to select suitable implants for different patients. To get these measurements, a segmentation method of cochlea medical images is needed. An important pre-processing step for good cochlea segmentation involves efficient image registration. The cochlea’s small size and complex structure, in addition to the different resolutions and head positions during imaging, reveals a big challenge for the automated registration of the different image modalities. In this paper, an Automatic Cochlea Image Registration (ACIR) method for multi- modal human cochlea images is proposed. This method is based on using small areas that have clear structures from both input images instead of registering the complete image. It uses the Adaptive Stochastic Gradient Descent Optimizer (ASGD) and Mattes’s Mutual Information metric (MMI) to estimate 3D rigid transform parameters. The use of state of the art medical image registration optimizers published over the last two years are studied and compared quantitatively using the standard Dice Similarity Coefficient (DSC). ACIR requires only 4.86 seconds on average to align cochlea images automatically and to put all the modalities in the same spatial locations without human interference. The source code is based on the tool elastix and is provided for free as a 3D Slicer plugin. Another contribution of this work is a proposed public cochlea standard dataset which can be downloaded for free from a public XNAT server.

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