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

• Published in: Computer methods and programs in biomedicine Vol. 106; no. 3; pp. 188 - 200
• Main Authors: Kalinić, Hrvoje, Lončarić, Sven, Čikeš, Maja, Miličić, Davor, Bijnens, Bart
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01.06.2012; Elsevier
• Subjects: Algorithms; Aorta - diagnostic imaging; Atlas-based segmentation; Biological and medical sciences; Blood Flow Velocity - physiology; Cardiac outflow velocity profile; Cardiac Output - physiology; Doppler ultrasound imaging; Echocardiography, Doppler; Humans; Image Processing, Computer-Assisted - methods; Image registration; Internal Medicine; Medical sciences; Other; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Segmentation propagation; Technology. Biomaterials. Equipments. Material. Instrumentation; Image registration; Atlas-based segmentation; Cardiac outflow velocity profile; Doppler ultrasound imaging; Segmentation propagation; Doppler ultrasound study; Segmentation; Image processing; Ultrasound imaging; Biomedical engineering
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2010.11.001

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.