Automated analysis of coronary lesions from cineangiograms using vessel tracking and iterative deconvolution techniques

Alperin, Noam; Hoffmann, K; Doi, Kunio; Chua, K. G.

doi:10.1109/cic.1989.130508

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…In the first variation of this basic tracking technique, the vector from the current to the identified next tracking point is monitored 24,34 ͑Fig. 2͒.…”

Section: Methodsmentioning

confidence: 99%

“…[14][15][16][17][18] Centerlines are an integral part of 3D analysis of vascular trees; for example, the 3D centerline obtained from biplane or multiple-projection data sets [19][20][21][22][23] is used for determination of imaging geometry via the bifurcation points. 22 Previous vessel tracking techniques were commonly semi-automated, and included manual tracing of the centerline, 3 as well as indication of several points, 22,23 or start and end points 4,[24][25][26][27] along the vessel. In these techniques, vessel pixels were identified along the path between the indicated points.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative evaluation of vessel tracking techniques on coronary angiograms

Sen

Doi

et al. 1999

Medical Physics

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Accurate, automated determination of vessel center lines is essential for two- and three-dimensional analysis of the coronary vascular tree. Therefore, we have been developing techniques for vessel tracking and for evaluating their accuracy and precision in clinical images. After points in vessels are manually indicated, the vessels are tracked automatically by means of a modified sector-search approach. The perimeters of sectors centered on previous tracking points are searched for the pixels with the maximum contrast. The sector size and radius are automatically adjusted based on local vessel tortuosity. The performance of the tracking technique in regions of high-intensity background is improved by application of a nonlinear adaptive filtering technique in which the vessel signal is effectively removed prior to background estimation. The tracking results were evaluated visually and by calculation of distances between the tracked and user-indicated centerlines, which were used as the "truth." Two hundred and fifty-six coronary vessels were tracked in 32 angiograms. Vessels as small as 0.6 mm in diameter were tracked accurately. This technique correctly tracked 255/256 (>99%) vessels based on an average of 2-3 indicated points per vessel. The one incorrect tracking result was due to a low signal-to-noise ratio (SNR<2). The distance between the tracked and the "true" centerlines ranged from 0.4 to 1.8 pixels, with an average of 0.8 pixels. These results indicate that this technique can provide a reliable basis for 2D and 3D vascular analysis.

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“…In the first variation of this basic tracking technique, the vector from the current to the identified next tracking point is monitored 24,34 ͑Fig. 2͒.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of vessel tracking techniques on coronary angiograms

Sen

Doi

et al. 1999

Medical Physics

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show abstract

“…These applications require that the pertinent vessels be tracked and digitized. Because manual tracking is tedious and impractical in a clinical environment, several investigators [37] have attempted to develop automatic or semi-automatic methods. However, most of these techniques are based on purely local criteria for vessel identification and suffer from errors due to noise, spurious vessel discontinuities, vessel branch points, and overlapping vessels.…”

Section: Tracking Blood Vessels In Angiogramsmentioning

confidence: 99%

"Brownian strings": Segmenting images with stochastically deformable contours

Grzeszczuk

Levin

1997

IEEE Trans. Pattern Anal. Machine Intell.

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This paper describes an image segmentation technique in which an arbitrarily shaped contour was deformed stochastically until it fitted around an object of interest. The evolution of the contour was controlled by a simulated annealing process which caused the contour to settle into the global minimum of an image-derived "energy" function. The nonparametric energy function was derived from the statistical properties of previously segmented images, thereby incorporating prior experience. Since the method was based on a state space search for the contour with the best global properties, it was stable in the presence of image errors which confound segmentation techniques based on local criteria, such as connectivity. Unlike "snakes" and other active contour approaches, the new method could handle arbitrarily irregular contours in which each interpixel crack represented an independent degree of freedom. Furthermore, since the contour evolved toward the global minimum of the energy, the method was more suitable for fully automatic applications than the snake algorithm, which frequently has to be reinitialized when the contour becomes trapped in local energy minima. High computational complexity was avoided by efficiently introducing a random local perturbation in a time independent of contour length, providing control over the size of the perturbation, and assuring that resulting shape changes were unbiased. The method was illustrated by using it to find the brain surface in magnetic resonance head images and to track blood vessels in angiograms. Additional information is available from http://mri.uchicago.edu.

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“…Low concentrations of contrast (25%) resulted in significant underestimation of average diameter when compared with concentrations of 50% and higher. Similar Newer approaches to edge detection may be more robust in the setting of poor opacification of a vessel [21]. The combination of digital subtraction techniques with QCA offers the theoretical opportunity to overcome the problems with poor opacification; however, a practical technique to apply subtraction methods to the dynamic images of coronary angiography remains elusive.…”

Section: Radiographic Factors That Affect Qca Accuracy and Variabilitymentioning

confidence: 99%

Sources of error in quantitative coronary angiography

Herrington

Siebes

Walford

1993

Cathet. Cardiovasc. Diagn.

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Many studies have reported the accuracy of quantitative coronary angiography (QCA) based on experiments using moderated-size phantoms imaged under unrealistic radiographic conditions. However, these observations may not be generalizable to the setting of clinical angiography. To determine QCA accuracy in a realistic radiographic setting and evaluate the impact of the x-ray system line spread function, plexiglass phantoms were imaged inside and out of a human thorax. A realistic radiographic background was associated with a 38% increase in variability of results (p < 0.05). Low concentrations of contrast and large image intensifier input screens were associated with significantly larger errors and variability in results (p < 0.05). There was a systematic overestimation of diameter in the smallest phantom. A mathematical model of the x-ray line spread function was developed that explains the observed overestimation of the smallest phantom and provide a rational approach for correction of the line spread function for QCA. Many factors encountered in clinical coronary angiography such as nonuniform radiographic background, low concentrations of contrast, and small vessel diameters have a significant adverse impact on the accuracy and/or variability of gradient-based edge detection QCA systems.

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Automated analysis of coronary lesions from cineangiograms using vessel tracking and iterative deconvolution techniques

Cited by 12 publications

References 8 publications

Quantitative evaluation of vessel tracking techniques on coronary angiograms

Quantitative evaluation of vessel tracking techniques on coronary angiograms

"Brownian strings": Segmenting images with stochastically deformable contours

Sources of error in quantitative coronary angiography

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