Intravascular ultrasound imaging of coronary arteries provides important information about coronary lumen, wall, and plaque characteristics. Quantitative studies of coronary atherosclerosis using intravascular ultraso...
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Intravascular ultrasound imaging of coronary arteries provides important 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 limited by the need for observers with substantial experience and the tedious nature of manual border detection. We have developed a method for segmentation of intravascular ultrasound images that identifies the internal and external elastic laminae and the plaque-lumen interface. The border detectionalgorithm was evaluated in a set of 38 intravascular ultrasound images acquired from fresh cadaveric hearts using a 30 MHz imaging catheter. To assess the performance of our border detection method we compared five quantitative measures of arterial anatomy derived from computer-detected borders with measures derived from borders 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 stenosis (r = 0.93, y = 0.99x- 1.34.) Computer-derived segmental plaque thickness measurements were highly accurate. Our knowledge-based intravascular ultrasound segmentation method shows substantial promise for the quantitative analysis of in vivo intravascular ultrasound image data.
The problem of edge detection in noisy images is addressed in this paper. It is shown that the performance of an existing edge detection method, known as the stochastic gradient operator, can be significantly improved...
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ISBN:
(纸本)0780331222
The problem of edge detection in noisy images is addressed in this paper. It is shown that the performance of an existing edge detection method, known as the stochastic gradient operator, can be significantly improved by incorporating three new features: (i) a robust technique for estimating the noise variance and autocorrelation function of the signal, (ii) a block-by-block adaptation of the gradient mask, and (iii) calculation of a threshold based on Rayleigh distribution. The performance of the proposed technique is compared with that of some existing ones.
This paper presents a method to quantitatively measure ocean surface movement, using sequential 10.8 mum band AVHRR images, developed at the University of Delaware in the spring of 1991. An ordered statistical edge de...
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This paper presents a method to quantitatively measure ocean surface movement, using sequential 10.8 mum band AVHRR images, developed at the University of Delaware in the spring of 1991. An ordered statistical edge detectionalgorithm is used to select ocean thermal pattern features by detecting and mapping gradients, at the same time discriminating between the water surface, land, and clouds. Use of edge detection to select features in this manner reduces the need to perform preprocess screening and masking to remove clouds and land. A constrained correlation based feature recognition scheme is then used to find the best match to the pattern feature in a subsequent image. Surface displacement direction and distance are calculated for each selected point with average period velocity being computed based on elapsed time. Study areas off the Delaware and New Jersey coast, and the California Current System off Northern California have been analyzed using this technique, with the results correlating favorably with in situ anchored buoy, and drifting buoy measurements.
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