scholarly journals Automatic border detection of intravascular ultrasound images

1996 ◽  
Vol 27 (2) ◽  
pp. 240
Author(s):  
Stephen P. Wiet ◽  
Stuart A. Greenfield ◽  
Reena Sinha ◽  
Gorav Ailawadi ◽  
Michael J. Vonesh ◽  
...  
Author(s):  
Jouke Dijkstra ◽  
Gerhard Koning ◽  
Joan C. Tuinenburg ◽  
Pranobe V. Oemrawsingh ◽  
Clemens von Birgelen ◽  
...  

2001 ◽  
Vol 1230 ◽  
pp. 916-922 ◽  
Author(s):  
Jouke Dijkstra ◽  
Gerhard Koning ◽  
Joan C. Tuinenburg ◽  
Pranobe V. Oemrawsingh ◽  
Clemens von Birgelen ◽  
...  

2005 ◽  
Vol 78 (926) ◽  
pp. 122-129 ◽  
Author(s):  
C V Bourantas ◽  
M E Plissiti ◽  
D I Fotiadis ◽  
V C Protopappas ◽  
G V Mpozios ◽  
...  

2003 ◽  
Vol 1256 ◽  
pp. 1111-1116 ◽  
Author(s):  
Jouke Dijkstra ◽  
Gerhard Koning ◽  
Joan C Tuinenburg ◽  
Pranobe V Oemrawsingh ◽  
Johan H.C Reiber

1996 ◽  
Vol 13 (6) ◽  
pp. 599-607 ◽  
Author(s):  
DIRK HAUSMANN ◽  
GUY FRIEDRICH ◽  
BOBBY SONI ◽  
WERNER G. DANIEL ◽  
PETER J. FITZGERALD ◽  
...  

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.


1995 ◽  
Vol 25 (2) ◽  
pp. 180A
Author(s):  
Marco Masseroli ◽  
Robert M. Cothren ◽  
E. Murat Tuzcu ◽  
Dominique S. Meier ◽  
James D. Thomas ◽  
...  

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