Lumen detection in human IVUS images using region-growing

To assess the health of arterial tissue in intravascular ultrasound (IVUS) images, detection of luminal borders is critical. The authors have enhanced two automated border detection schemes using region-growing based on inter-pixel gray-scale differences, components-labeling (CL) and dilation-erosio...

Full description

Saved in:
Bibliographic Details
Published inComputers in Cardiology 1996 pp. 37 - 40
Main Authors Brathwaite, P.A., Chandran, K.B., McPherson, D.D., Dove, E.L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1996
Subjects
Calcium
Gray-scale
Humans
Image analysis
Image segmentation
Leak detection
Lesions
Pixel
Shape
Ultrasonic imaging
Online AccessGet full text

Cover

More Information
Summary:To assess the health of arterial tissue in intravascular ultrasound (IVUS) images, detection of luminal borders is critical. The authors have enhanced two automated border detection schemes using region-growing based on inter-pixel gray-scale differences, components-labeling (CL) and dilation-erosion, and watershed segmentation (WS) to correct for leaked regions due to signal drop-out and strut artifacts. The two methods were evaluated using 8 IVUS images with and without calcium lesions. Shapes were quantitatively analyzed, and the cross-sectional lumen areas calculated from the two automated methods were compared with the areas from expert traced images. Algorithm execution times were also compared. Results: CL vs. expert traced had a mean area difference of 7 pixels (p>0.05), WS vs. expert traced had a mean area difference of 394 pixels (p<0.05), for the leaked images. Thus region-growing with CL accurately predicts luminal areas of the artery and corrects for luminal leaks better than WS.
ISBN:0780337107
9780780337107
ISSN:0276-6547
DOI:10.1109/CIC.1996.542467