21 March 2014 Normal distributions transform in multi-modal image registration of optical coherence tomography and computed tomography datasets
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Abstract
In recent years, optical coherence tomography (OCT) has gained increasing attention not only as an imaging device, but also as a navigation system for surgical interventions. This approach demands to register intraoperative OCT to pre-operative computed tomography (CT) data. In this study, we evaluate algorithms for multi-modal image registration of OCT and CT data of a human temporal bone specimen. We focus on similarity measures that are common in this field, e.g., normalized mutual information, normalized cross correlation, and iterative closest point. We evaluate and compare their accuracies to the relatively new normal distribution transform (NDT), that is very common in simultaneous localization and mapping applications, but is not widely used in image registration. Matching is realized considering appropriate image pre-processing, the aforementioned similarity measures, and local optimization algorithms, as well as line search optimization. For evaluation purpose, the results of a point-based registration with fiducial landmarks are regarded as ground truth. First results indicate that state of the art similarity functions do not perform with the desired accuracy, when applied to unprocessed image data. In contrast, NDT seems to achieve higher registration accuracy.
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Jesús Díaz Díaz, Mauro H. Riva, Omid Majdani, Tobias Ortmaier, "Normal distributions transform in multi-modal image registration of optical coherence tomography and computed tomography datasets", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90343L (21 March 2014); doi: 10.1117/12.2043623; https://doi.org/10.1117/12.2043623
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