Registration Methods for Pulmonary Image Analysis - Integration of Morphological and Physiological Knowledge (Paperback, 2014 ed.)


Various applications in the field of pulmonary image analysis require a registration of CT images of the lung. For example, a registration-based estimation of the breathing motion is employed to increase the accuracy of dose distribution in radiotherapy. Alexander Schmidt-Richberg develops methods to explicitly model morphological and physiological knowledge about respiration in algorithms for the registration of thoracic CT images. The author focusses on two lung-specific issues: on the one hand, the alignment of the interlobular fissures and on the other hand, the estimation of sliding motion at the lung boundaries. He shows that by explicitly considering these aspects based on a segmentation of the respective structure, registration accuracy can be significantly improved.


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Product Description

Various applications in the field of pulmonary image analysis require a registration of CT images of the lung. For example, a registration-based estimation of the breathing motion is employed to increase the accuracy of dose distribution in radiotherapy. Alexander Schmidt-Richberg develops methods to explicitly model morphological and physiological knowledge about respiration in algorithms for the registration of thoracic CT images. The author focusses on two lung-specific issues: on the one hand, the alignment of the interlobular fissures and on the other hand, the estimation of sliding motion at the lung boundaries. He shows that by explicitly considering these aspects based on a segmentation of the respective structure, registration accuracy can be significantly improved.

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Product Details

General

Imprint

Springer Vieweg

Country of origin

Germany

Series

Aktuelle Forschung Medizintechnik - Latest Research in Medical Engineering

Release date

February 2014

Availability

Expected to ship within 10 - 15 working days

First published

2014

Authors

Dimensions

210 x 148 x 10mm (L x W x T)

Format

Paperback

Pages

168

Edition

2014 ed.

ISBN-13

978-3-658-01661-6

Barcode

9783658016616

Categories

LSN

3-658-01661-2



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