Automated contour detection for high-frequency intravascular ultrasound imaging: a technique with blood noise reduction for edge enhancement.
Automated edge detection may standardize measurements among observers, providing for rapid assessment of intravascular ultrasound (IVUS) images. However, with high frequency images, enhanced blood signals make it difficult to define and trace the lumen borders. Accordingly, we evaluated a fully automated contour analysis facilitated with a blood noise reduction algorithm (BNR) for 40-MHz IVUS images in human coronary arteries of 27 patients. This algorithm is based on the principle that blood echo speckles have higher temporal and spatial variations than the arterial wall. A total of 193 paired lumen areas and 78 external elastic membrane (EEM) areas were measured and compared. Automated measurements showed good agreement with manual tracings for lumen and EEM area, with high correlation coefficients (0.945 and 0.950, respectively) and small variability (0.4 +/- 14.4% and 0.6 +/- 9.7%, respectively). This preliminary finding suggests that automated contour detection facilitated with BNR appeared to be a feasible and reliable technique for area measurements in 40-MHz IVUS imaging.
Takagi
A
A
Division of Cardiovascular Medicine, Stanford University Medical Center, Stanford, CA 94305, USA.
Hibi
K
K
Zhang
X
X
Teo
T J
TJ
Bonneau
H N
HN
Yock
P G
PG
Fitzgerald
P J
PJ
D001769:Blood; D003331:Coronary Vessels / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D018084:Ultrasonography, Interventional