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Assessment of image features for vessel wall segmentation in intravascular ultrasound images

Assessment of image features for vessel wall segmentation in intravascular ultrasound images Int J CARS (2016) 11:1397–1407 DOI 10.1007/s11548-015-1345-4 ORIGINAL ARTICLE Assessment of image features for vessel wall segmentation in intravascular ultrasound images 1,2 1,2 1,3 Lucas Lo Vercio · José Ignacio Orlando · Mariana del Fresno · 1,2 Ignacio Larrabide Received: 13 January 2015 / Accepted: 24 December 2015 / Published online: 25 January 2016 © CARS 2016 Abstract publicly available dataset, reaching values of AUC-PR up Background Intravascular ultrasound (IVUS) provides axial to 0.97 and Jaccard index close to 0.85. greyscale images, allowing the assessment of the vessel wall Conclusion Noise-reduction filters and Haralick’s textural and the surrounding tissues. Several studies have described features denoted their relevance to identify lumen and back- automatic segmentation of the luminal boundary and the ground. Laws’ textural features, local binary patterns, Gabor media–adventitia interface by means of different image fea- filters and edge detectors had less relevance in the selection tures. process. Purpose The aim of the present study is to evaluate the capability of some of the most relevant state-of-the-art image Keywords IVUS · Vessel wall · Segmentation · Feature features for segmenting IVUS images. The study is focused selection · SVM on Volcano 20 MHz frames not containing plaque or contain- http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computer Assisted Radiology and Surgery Springer Journals

Assessment of image features for vessel wall segmentation in intravascular ultrasound images

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References (43)

Publisher
Springer Journals
Copyright
Copyright © 2016 by CARS
Subject
Medicine & Public Health; Imaging / Radiology; Surgery; Health Informatics; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Science, general
ISSN
1861-6410
eISSN
1861-6429
DOI
10.1007/s11548-015-1345-4
pmid
26811082
Publisher site
See Article on Publisher Site

Abstract

Int J CARS (2016) 11:1397–1407 DOI 10.1007/s11548-015-1345-4 ORIGINAL ARTICLE Assessment of image features for vessel wall segmentation in intravascular ultrasound images 1,2 1,2 1,3 Lucas Lo Vercio · José Ignacio Orlando · Mariana del Fresno · 1,2 Ignacio Larrabide Received: 13 January 2015 / Accepted: 24 December 2015 / Published online: 25 January 2016 © CARS 2016 Abstract publicly available dataset, reaching values of AUC-PR up Background Intravascular ultrasound (IVUS) provides axial to 0.97 and Jaccard index close to 0.85. greyscale images, allowing the assessment of the vessel wall Conclusion Noise-reduction filters and Haralick’s textural and the surrounding tissues. Several studies have described features denoted their relevance to identify lumen and back- automatic segmentation of the luminal boundary and the ground. Laws’ textural features, local binary patterns, Gabor media–adventitia interface by means of different image fea- filters and edge detectors had less relevance in the selection tures. process. Purpose The aim of the present study is to evaluate the capability of some of the most relevant state-of-the-art image Keywords IVUS · Vessel wall · Segmentation · Feature features for segmenting IVUS images. The study is focused selection · SVM on Volcano 20 MHz frames not containing plaque or contain-

Journal

International Journal of Computer Assisted Radiology and SurgerySpringer Journals

Published: Jan 25, 2016

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