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1 March 20112D-3D registration using gradient-based MI for image guided surgery systems
Registration of preoperative CT data to intra-operative video images is necessary not only to compare the outcome
of the vocal fold after surgery with the preplanned shape but also to provide the image guidance for fusion of all imaging
modalities. We propose a 2D-3D registration method using gradient-based mutual information. The 3D CT scan is
aligned to 2D endoscopic images by finding the corresponding viewpoint between the real camera for endoscopic images
and the virtual camera for CT scans. Even though mutual information has been successfully used to register different
imaging modalities, it is difficult to robustly register the CT rendered image to the endoscopic image due to varying light
patterns and shape of the vocal fold. The proposed method calculates the mutual information in the gradient images as
well as original images, assigning more weight to the high gradient regions. The proposed method can emphasize the
effect of vocal fold and allow a robust matching regardless of the surface illumination. To find the viewpoint with
maximum mutual information, a downhill simplex method is applied in a conditional multi-resolution scheme which
leads to a less-sensitive result to local maxima. To validate the registration accuracy, we evaluated the sensitivity to
initial viewpoint of preoperative CT. Experimental results showed that gradient-based mutual information provided
robust matching not only for two identical images with different viewpoints but also for different images acquired before
and after surgery. The results also showed that conditional multi-resolution scheme led to a more accurate registration
than single-resolution.
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Yeny Yim, Xuanyi Chen, Mike Wakid, Steve Bielamowicz, James Hahn, "2D-3D registration using gradient-based MI for image guided surgery systems," Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 79642Q (1 March 2011); https://doi.org/10.1117/12.878245