Title :
MVN_CNN and UBN_CNN for endocardial edge detection
Author :
Ketout, H. ; Gu, Jhen-Fong ; Horne, Gary
Author_Institution :
Electr. & Comput. Eng., Dalhousie Univ., Halifax, NS, Canada
Abstract :
In this paper, Universal Binary Neurons Cellular Neural Networks (UBN_CNN) endocardial edge detection is proposed. The echocardiographic image is preprocessed to enhance the contrast and smoothness by utilizing Multi Valued Neural Cellular Neural Networks (MVN_CNN) non linear filter. UBN_CNN is applied to the smoothed image to extract the heart boundaries. A non threshold Boolean function with nine variables is utilized to detect the edges corresponding to the upward and downward brightness overleaps. Some experimental results are given for different echocardiographic images. The combination of MVN_CNN and UBN_CNN approach showed better results for extracting the LV endocardial boundaries.
Keywords :
Boolean functions; biomedical ultrasonics; cellular neural nets; edge detection; feature extraction; image enhancement; medical image processing; nonlinear filters; LV endocardial boundary extraction; MVN_CNN; UBN_CNN; contrast enhancement; echocardiographic image; endocardial edge detection; heart boundary extraction; multivalued neural cellular neural networks; nonlinear filter; nonthreshold Boolean function; smoothness enhancement; universal binary neurons cellular neural networks; Cellular neural networks; Equations; Heart; Image edge detection; Neurons; Nonlinear filters; CNN; Echocardiography; Endocardial; MVN_CNN; UBN_CNN; artifacts; edge detection;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022163