DocumentCode
554034
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
Volume
2
fYear
2011
fDate
26-28 July 2011
Firstpage
781
Lastpage
785
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022163
Filename
6022163
Link To Document