Full orientation invarlance and improved feature selectivity of 3D SIFT with application to medical image analysis

Stephane Allaire, John J. Kim, Stephen L. Breen, David A. Jaffray, Vladimir Pekar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

103 Scopus citations

Abstract

This paper presents a comprehensive extension of the Scale Invariant Feature Transform (SIFT), originally introduced in 21), 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.

Original languageEnglish (US)
Title of host publication2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops - Anchorage, AK, United States
Duration: Jun 23 2008Jun 28 2008

Publication series

Name2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

Conference

Conference2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
Country/TerritoryUnited States
CityAnchorage, AK
Period6/23/086/28/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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