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27 March 2009 A method for registration and model-based segmentation of Doppler ultrasound images
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Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72590S (2009) https://doi.org/10.1117/12.811331
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Morphological changes of Doppler ultrasound images are an important source of information for diagnosis of cardiovascular diseases. Quantification of these flow profiles requires segmentation of the ultrasound images. In this article, we propose a new model-based method for segmentation of (aortic outflow) velocity profiles. The method is based on a procedure for registration using a geometric transformation specifically designed for matching Doppler ultrasound profiles. After manual segmentation of a model image, the model image is temporarily registered to a new image using two manually defined points in time. Next, a non-rigid registration was carried out in the velocity direction. As a similarfity measure normalized mutual information is used, while optimization is performed by a genetic algorithm. The registration method is experimentally validated using an in-silico image phantom, and showed an accuracy of 5.4%. The model based on segmentation is evaluated in a seris of aortic outflow Doppler ultrasound images from 30 normal volunteers. Comparing the automated method to the manual delineation by an expert cardiologist the method proved accurate to 6.6%. The experimental results confirm the accuracy of the approach and shows that the method can be used for the segmentation of the clinically obtained aortic outflow velocity profiles.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hrvoje Kalinić, Sven Lončarić, Maja Čikeš M.D., Davor Milicic M.D., Ivo Čikeš M.D., George Sutherland M.D., and Bart Bijnens "A method for registration and model-based segmentation of Doppler ultrasound images", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72590S (27 March 2009); https://doi.org/10.1117/12.811331
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