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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Stephane Allaire, Radiation Medicine Program, Princess Margaret Hospital, Toronto, ON, Canada
John J. Kim, Radiation Medicine Program, Princess Margaret Hospital, Toronto, ON, Canada
Stephen L. Breen, Radiation Medicine Program, Princess Margaret Hospital, Toronto, ON, Canada
David A. Jaffray, Radiation Medicine Program, Princess Margaret Hospital, Toronto, ON, Canada
Vladimir Pekar, Philips Research North America, Markham, ON, Canada
This paper presents a comprehensive extension of the Scale Invariant Feature Transform (SIFT), originally introduced in 2D, to volumetric images. While tackling the significant computational efforts required by such multiscale processing of large data volumes, our implementation addresses two important mathematical issues related to the 2D-to-3D extension. It includes efficient steps to filter out extracted point candidates that have low contrast or are poorly localized along edges or ridges. In addition, it achieves, for the first time, full 3D orientation invariance of the descriptors, which is essential for 3D feature matching. An application of this technique is demonstrated to the feature-based automated registration and segmentation of clinical datasets in the context of radiation therapy.
Citation:
Stephane Allaire, John J. Kim, Stephen L. Breen, David A. Jaffray, Vladimir Pekar, "Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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