Principal curves to extract vessels in 3D angiograms

Wong, Wilbur C.K.; Chung, Albert C. S.

doi:10.1109/cvprw.2008.4563015

Cited by 10 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These can be roughly divided into parametric model fitting and cross-section based methods. Methods based on parametric models try to fit a tube-like model into the lumen, exploiting the fact that vessels are elongated and roughly tubular [9,8,5]. These methods work very well on regular and uniform vessels, but a cylindrical model is not well suited to capture subtle variations in lumen calibre, as the ones consequence of mild stenotic symptoms.…”

Section: Introductionmentioning

confidence: 98%

GroBa: Growing balloons for calibre measurement on stenotic lumens

Perez-Rovira

Trucco

Weir‐McCall

et al. 2012

2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

View full text Add to dashboard Cite

This papers describes GroBa, a new lumen calibre measurement technique based on growing balloons. GroBa presents the advantages of cross-sectional based methods, as it is able to cope with irregular, non-tubular vessel structures, such as stenosis or aneurysms, but at the same time it is able to obtain precise calibre measurements even when the estimated centrelines are not accurate. Experimental results using phantoms and real subtracted full-body magnetic resonance angiograms show the potential of this work. GroBa is integrated into a fully automatic system that segments the vasculature, obtains its centrelines, measures the lumen calibre at each detected artery, and presents the calibre information in false colours in the maximum intensity projection exploiting the HSV colour-space; all without any human intervention.

show abstract

Section: Introductionmentioning

confidence: 98%

GroBa: Growing balloons for calibre measurement on stenotic lumens

Perez-Rovira

Trucco

Weir‐McCall

et al. 2012

2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

View full text Add to dashboard Cite

show abstract

“…Another property of is symmetry. Consider the pmf value at , , we find that the mean pmf is symmetric with respect to the lumen center along the normal direction, i.e., (15) Since is symmetric, . We attempted to replicate the derivation for the Rician noise (a commonly used noise model in magnetic resonance imaging).…”

Section: Appendix a Correlation-based Pmfmentioning

confidence: 99%

“…An algorithm based on the theory of principal curves is introduced to refine the initial model, find centerlines of the network, and determine the lumen (i.e., flow channel) widths in a single execution. An earlier version of this paper was presented in [15]. The closest work is the semiautomatic extraction algorithm in [16].…”

Section: Introductionmentioning

confidence: 99%

Principal Curves for Lumen Center Extraction and Flow Channel Width Estimation in 3-D Arterial Networks: Theory, Algorithm, and Validation

Wong

Chung

2012

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-We present an energy-minimization-based framework for locating the centerline and estimating the width of tubelike objects from their structural network with a nonparametric model. The nonparametric representation promotes simple modeling of nested branches and -way furcations, i.e., structures that abound in an arterial network, e.g., a cerebrovascular circulation. Our method is capable of extracting the entire vascular tree from an angiogram in a single execution with a proper initialization. A succinct initial model from the user with arterial network inlets, outlets, and branching points is sufficient for complex vasculature. The novel method is based upon the theory of principal curves. In this paper, theoretical extension to grayscale angiography is discussed, and an algorithm to find an arterial network as principal curves is also described. Quantitative validation on a number of simulated data sets, synthetic volumes of 19 BrainWeb vascular models, and 32 Rotterdam Coronary Artery volumes was conducted. We compared the algorithm to a state-of-the-art method and further tested it on two clinical data sets. Our algorithmic outputs-lumen centers and flow channel widths-are important to various medical and clinical applications, e.g., vasculature segmentation, registration and visualization, virtual angioscopy, and vascular atlas formation and population study.

show abstract

“…Roughly, the purpose is to search for a curve passing through the middle of the observations, as illustrated in Figure 1. Principal curves have a broad range of applications in many different areas, such as physics (Hastie and Stuetzle [26], Friedsam and Oren [23]), character and speech recognition (Kégl and Krzyżak [29], Reinhard and Niranjan [39]), mapping and geology (Brunsdon [10], Stanford and Raftery [43], Banfield and Raftery [4], Einbeck, Tutz and Evers [20,21]), natural sciences (De'ath [14], Corkeron, Anthony and Martin [13], Einbeck, Tutz and Evers [20]) and medicine (Wong and Chung [46], Caffo, Crainiceanu, Deng and Hendrix [11]). …”

Section: Principal Curvesmentioning

confidence: 99%

Parameter Selection for Principal Curves

Biau

Fischer

2012

IEEE Trans. Inform. Theory

View full text Add to dashboard Cite

-Principal curves are nonlinear generalizations of the notion of first principal component. Roughly, a principal curve is a parameterized curve in R d which passes through the "middle" of a data cloud drawn from some unknown probability distribution. Depending on the definition, a principal curve relies on some unknown parameters (number of segments, length, turn. . . ) which have to be properly chosen to recover the shape of the data without interpolating. In the present paper, we consider the principal curve problem from an empirical risk minimization perspective and address the parameter selection issue using the point of view of model selection via penalization. We offer oracle inequalities and implement the proposed approaches to recover the hidden structures in both simulated and real-life data.

show abstract

Principal curves to extract vessels in 3D angiograms

Cited by 10 publications

References 23 publications

GroBa: Growing balloons for calibre measurement on stenotic lumens

GroBa: Growing balloons for calibre measurement on stenotic lumens

Principal Curves for Lumen Center Extraction and Flow Channel Width Estimation in 3-D Arterial Networks: Theory, Algorithm, and Validation

Parameter Selection for Principal Curves

Contact Info

Product

Resources

About