Image registration and atlas-based segmentation of cardiac outflow velocity profiles

Authors:
Hrvoje Kalinić;Sven Lončarić;Maja ikeš;Davor Miličić;Bart Bijnens
Affiliations:
Faculty of Electrical Engineering and Computing, Department of Electronic Systems and Information Processing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia;Faculty of Electrical Engineering and Computing, Department of Electronic Systems and Information Processing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia;Department for Cardiovascular Diseases, University Hospital Centre Zagreb, 10000 Zagreb, Croatia;Department for Cardiovascular Diseases, University Hospital Centre Zagreb, 10000 Zagreb, Croatia;Catalan Institution for Research and Advanced Studies (ICREA) and Universitat Pompeu Fabra (CISTIB), E08003 Barcelona, Spain
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

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Abstract

Cardiovascular disease is the leading cause of death worldwide and for this reason computer-based diagnosis of cardiac diseases is a very important task. In this article, a method for segmentation of aortic outflow velocity profiles from cardiac Doppler ultrasound images is presented. The proposed method is based on the statistical image atlas derived from ultrasound images of healthy volunteers. The ultrasound image segmentation is done by registration of the input image to the atlas, followed by a propagation of the segmentation result from the atlas onto the input image. In the registration process, the normalized mutual information is used as an image similarity measure, while optimization is performed using a multiresolution gradient ascent method. The registration method is evaluated using an in-silico phantom, real data from 30 volunteers, and an inverse consistency test. The segmentation method is evaluated using 59 images from healthy volunteers and 89 images from patients, and using cardiac parameters extracted from the segmented image. Experimental validation is conducted using a set of healthy volunteers and patients and has shown excellent results. Cardiac parameter segmentation evaluation showed that the variability of the automated segmentation relative to the manual is comparable to the intra-observer variability. The proposed method is useful for computer aided diagnosis and extraction of cardiac parameters.