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Segmentation of pulmonary vascular tree from 3D CT thorax scans

Paper ID Volume ID Publish Year Pages File Format Full-Text
5201 349 2015 14 PDF Available
Title
Segmentation of pulmonary vascular tree from 3D CT thorax scans
Abstract

This paper considers the problem of pulmonary vessels identification in thoracic 3D CT scans. In particular, the method for pulmonary vascular tree segmentation is introduced. The main idea behind the introduced method is to extract both thoracic trees together (i.e. the vascular tree and the airway tree) and then remove airway walls. Therefore, firstly segmentation of vessels and airway walls is performed using 3D region growing where the growth of the region is guided and constrained by results of random walk segmentation applied to consecutive CT slices. In particular, results of random walk segmentation of one slice are used to determine seeds for random walk segmentation of the following slice. Next step is airway tree segmentation using 3D region growing algorithm guided and constrained by the morphological gradient. Finally, morphological processing is applied in order to extend airway lumen onto airway walls and remove the overlapping regions. The main steps of the proposed approach are described in detail. Results of pulmonary vascular tree segmentation from example thoracic volumetric CT datasets provided by the introduced approach are presented and discussed. Based on a manually selected and radiologist's verified ground truth pixels and the resulting quality measures it can be concluded, that the average accuracy of the introduced approach is about 90%.

Keywords
Pulmonary vascular tree; Airway tree; 3D CT; Image segmentation; Region growing; Random walk segmentation
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Segmentation of pulmonary vascular tree from 3D CT thorax scans
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Publisher
Database: Elsevier - ScienceDirect
Journal: Biocybernetics and Biomedical Engineering - Volume 35, Issue 2, 2015, Pages 106–119
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us