Graph-based cross-sectional intravascular image segmentation

Ehab Essa; Xianghua Xie; Huaizhong Zhang; James Cotton; Dave Smith

doi:10.1088/2053-2563/ab01fach6

Graph-based cross-sectional intravascular image segmentation

Ehab Essa^*, Xianghua Xie, Huaizhong Zhang, James Cotton, Dave Smith

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

We present a fully automatic segmentation approach to detect the media–adventitia border in intravascular ultrasound (IVUS) and the lumen border in optical tomography (OCT) images. A graph-based segmentation method is developed to accurately estimate the borders.

Original language	English
Title of host publication	Stent applications
Publisher	Institute of Physics Publishing
Chapter	6
Pages	1-26
Number of pages	26
Volume	1
ISBN (Electronic)	9780750319966
ISBN (Print)	9780750319959
DOIs	https://doi.org/10.1088/2053-2563/ab01fach6
Publication status	Published - 6 Aug 2019

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title = "Graph-based cross-sectional intravascular image segmentation",

abstract = "We present a fully automatic segmentation approach to detect the media–adventitia border in intravascular ultrasound (IVUS) and the lumen border in optical tomography (OCT) images. A graph-based segmentation method is developed to accurately estimate the borders.",

author = "Ehab Essa and Xianghua Xie and Huaizhong Zhang and James Cotton and Dave Smith",

year = "2019",

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AU - Cotton, James

AU - Smith, Dave

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Y1 - 2019/8/6

N2 - We present a fully automatic segmentation approach to detect the media–adventitia border in intravascular ultrasound (IVUS) and the lumen border in optical tomography (OCT) images. A graph-based segmentation method is developed to accurately estimate the borders.

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BT - Stent applications

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Graph-based cross-sectional intravascular image segmentation

Abstract

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