M. Sonka et al., SEGMENTATION OF INTRAVASCULAR ULTRASOUND IMAGES - A KNOWLEDGE-BASED APPROACH, IEEE transactions on medical imaging, 14(4), 1995, pp. 719-732
Citations number
51
Categorie Soggetti
Engineering, Biomedical","Radiology,Nuclear Medicine & Medical Imaging
Intravascular ultrasound imaging of coronary arteries provides importa
nt information about coronary lumen, wall, and plaque characteristics.
Quantitative studies of coronary atherosclerosis using intravascular
ultrasound and manual identification of wall and plaque borders are li
mited by the need for observers with substantial experience and the te
dious nature of manual border detection. We have developed a method fo
r segmentation of intravascular ultrasound images that identifies the
internal and external elastic laminae and the plaque-lumen interface.
The border detection algorithm was evaluated in a set of 38 intravascu
lar ultrasound images acquired from fresh cadaveric hearts using a 30
MHz imaging catheter. To assess the performance of our border detectio
n method we compared five quantitative measures of arterial anatomy de
rived from computer-detected borders with measures derived from border
s manually defined by expert observers. Computer-detected and observer
-defined lumen areas correlated very well (r = 0.96, y = 1.02x+ 0.52),
as did plaque areas (r = 0.95, y = 1.07x- 0.48), and percent area ste
nosis (r = 0.93, y = 0.99x- 1.34.) Computer-derived segmental plaque t
hickness measurements were highly accurate. Our knowledge-based intrav
ascular ultrasound segmentation method shows substantial promise for t
he quantitative analysis of in vivo intravascular ultrasound image dat
a.