Intensity-based 2D-3D Medical Image Registration | Algorithms and Analysis

Daniel Russakoff

ISBN 10: 3639119541 / ISBN 13: 9783639119541

Published by VDM Verlag Dr. Müller, 2010

About this Item

Intensity-based 2D-3D Medical Image Registration | Algorithms and Analysis | Daniel Russakoff | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2010 | VDM Verlag Dr. Müller | EAN 9783639119541 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: Intensity-based 2D-3D Medical Image Registration | Algorithms and Analysis
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2010
• Language: English
• ISBN 10: 3639119541
• ISBN 13: 9783639119541
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 8W x 150H x 220D millimeters 8 millimeters width by 150 millimeters height by 220 millimeters depth
• Weight: 196 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Intensity-based 2D-3D medical image registration is a special case of the pose estimation problem from computer vision with many applications in medicine. This work presents an overview of the 2D-3D intensity-based image registration problem in the medical domain as well as results from several methods developed to aid in its practice. In particular: 1) Light field rendering techniques from the graphics community are extended to rapidly generate digitally reconstructed radiographs (DRRs). 2) A full 2D-3D registration algorithm using light field DRRs is presented and validated against a real, clinical gold standard. 3) A new, hybrid similarity measure is presented that is a weighted combination of an intensity-based image similarity measure and a point-based measure incorporating a single fiducial marker. 4) Finally, a novel similarity measure called regional mutual information (RMI) is introduced. RMI is an extension of mutual information which incorporates spatial information in a principled way. The additional spatial information helps make its use as a similarity measure much more robust to initial misregistration than standard mutual information.