Vascular Section Estimation in Medical Images Using Combined Feature Detection and Evolutionary Optimization

Authors: Iván Macía and Manuel Graña

Date: 11.09.2012


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Abstract

Accurate detection and extraction of 3D vascular structures is a crucial step for many medical image applications that require vascular analysis. Vessel tracking algorithms iteratively follow vascular branches point by point, obtaining geometric descriptors, such as centerlines and sections of branches, that describe patient-specific vasculature. In order to obtain these descriptors, most approaches use specialized scaled vascular feature detectors. However, these detectors may fail due to the presence of nearby spurious structures, incorrect scale or parameter choice or other undesired effects, obtaining incorrect local sections which may lead to unrecoverable errors during the tracking procedure. We propose to combine this approach with an evolutionary optimization framework that use specific modified vascular detectors as cost functions in order to obtain accurate vascular sections when the direct detection approach fails. We demonstrate the validity of this new approach with experiments using real datasets. We also show that, for a family of medialness functions, the procedure can be performed at fixed small scales which is computationally efficient for local kernel-based estimators.

BIB_text

@Article {
author = {Iván Macía and Manuel Graña},
title = {Vascular Section Estimation in Medical Images Using Combined Feature Detection and Evolutionary Optimization},
pages = {503-513},
volume = {7209},
keywds = {
evolutionary optimization, feature detectors, medialness, medical image analysis, section estimator, vascular analysis, vascular tracking, vesse
}
abstract = {
Accurate detection and extraction of 3D vascular structures is a crucial step for many medical image applications that require vascular analysis. Vessel tracking algorithms iteratively follow vascular branches point by point, obtaining geometric descriptors, such as centerlines and sections of branches, that describe patient-specific vasculature. In order to obtain these descriptors, most approaches use specialized scaled vascular feature detectors. However, these detectors may fail due to the presence of nearby spurious structures, incorrect scale or parameter choice or other undesired effects, obtaining incorrect local sections which may lead to unrecoverable errors during the tracking procedure. We propose to combine this approach with an evolutionary optimization framework that use specific modified vascular detectors as cost functions in order to obtain accurate vascular sections when the direct detection approach fails. We demonstrate the validity of this new approach with experiments using real datasets. We also show that, for a family of medialness functions, the procedure can be performed at fixed small scales which is computationally efficient for local kernel-based estimators.
}
isbn = {978-3-642-28930-9},
isi = {1},
date = {2012-09-11},
year = {2012},
}
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