Abstract
The heart has a complicated anatomy and is in constant movement. The cardiologist use echocardiogram to visualise the anatomy and its movement. It is difficult for the cardiologist to prognosticate or affirm the diseases like heart muscle damage, valvular problems, etc. due to presence of less information in echocardiograms. In this paper a system is proposed which automatically segments the left ventricle from the given echocardiogram video sequences using the combination of fuzzy C-means clustering and morphological operations and from which the left ventricle parameters and shape features are evoked. These features are then employed to linear discriminant analysis, K-nearest neighbour and Hopfield neural network to determine whether the heart is normal or affected with DCM. With LV parameters evaluated and shape features extracted it was found that HNN was able to model normal and abnormal hearts very well with an accuracy of 88% compared to LDA and K-NN.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Biomedical Engineering and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.