An Automated Method for Lumen and Media–Adventitia Border Detection in a Sequence of IVUS Frames

Plissiti, Marina E.; Fotiadis, Dimitrios I.; Michalis, Lampros K.; Bozios, G.

doi:10.1109/titb.2004.828889

Cited by 90 publications

(56 citation statements)

References 16 publications

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“…Many of the early approaches were based on the use of local properties of the image such as pixel intensity and gradient information (edges) combined with computational methods including graph search (Sonka et al (1995), von Birgelen et al (1996, Zhang et al (1998)), active surfaces (Klingensmith et al (2000)), active contours (Kovalski et al (2000)), and neural networks (Plissiti et al (2004)). In later approaches, segmentation was accomplished by the use of region and global information including texture (Mojsilovic et al (1997)), gray level variances (Haas et al (2000), Luo et al (2003)) contrast between the regions (Hui- Zhu et al (2002)), statistical properties of the image modeled by Rayleigh distributions using 2D (Haas et al (2000), Brusseau et al (2004)) and 3D information (Cardinal et al (2006)), and by mathematical morphology techniques (dos Santos Filho et al (2006)).…”

Section: Previous Workmentioning

confidence: 99%

Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach

Mendizabal-Ruiz

Rivera

Kakadiaris

2013

Medical Image Analysis

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Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels and is particularly useful for studying atherosclerosis. In this paper, we present a computational method for the delineation of the luminal border in IVUS B-mode images. The method is based in the minimization of a probabilistic cost function (that deforms a parametric curve) which defines a probability field that is regularized with respect to the given likelihoods of the pixels belonging to blood and non-blood. These likelihoods are obtained by a Support Vector Machine classifier trained using samples of the lumen and non-lumen regions provided by the user in the first frame of the sequence to be segmented. In addition, an optimization strategy is introduced in which the direction of the steepest descent and Broyden-Fletcher-Goldfarb-Shanno optimization methods are linearly combined to improve convergence. Our proposed method (MRK) is capable of segmenting IVUS B-mode images from different systems and transducer frequencies without the need of any parameter tuning, and it is robust with respect to changes of the B-mode reconstruction parameters which are subjectively adjusted by the interventionist. We validated the proposed method on six 20 MHz and six 40 MHz IVUS stationary sequences corresponding to regions with different degrees of stenosis, and evaluated its performance by comparing the * Corresponding author segmentation results with manual segmentation by two observers. Furthermore, we compared our method with the segmentation results on the same sequences as provided by the authors of three other segmentation methods available in the literature. The performance of all methods was quantified using Dice and Jaccard similarity indexes, Hausdorff distance, linear regression and Bland-Altman analysis. The results indicate the advantages of our method for the segmentation of the lumen contour.

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Section: Previous Workmentioning

confidence: 99%

Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach

Mendizabal-Ruiz

Rivera

Kakadiaris

2013

Medical Image Analysis

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“…Cardinal et al present a 3D IVUS segmentation where Rayleigh probability density functions (PDFs) are applied for modeling the pixel gray value distribution of the vessel wall structures [10]. An automated approach based on deformable models has been reported by Plissiti et al [11], who employed a Hopfield neural network for the modification and minimization of an energy function as well as a priori vessel geometry knowledge. Unal et al proposed in [12] a shape-driven approach to the segmentation of IVUS images, based on building a shape space using training data and consequently constraining the lumen and media-adventitia contours to a smooth, closed geometry in this space.…”

Section: Related Workmentioning

confidence: 99%

“…The latter points are defined in this work as those which satisfy the following equations: r = max {C( )} + 1 (11) r = max {C( )} 1 (12) For the above points in the 2D space, function f is defined as the signed Euclidean distance from the initialized contour for = const , i.e. f ( ,r C( )) = r C( ) (13) Following the definition of f , the FastRBF library [16] was used to generate the smooth contour approximation c ' by removing duplicate points where f has been defined (i.e.…”

Section: Rbf-based Contour Refinementmentioning

confidence: 99%

Texture Analysis and Radial Basis Function Approximation for IVUS Image Segmentation

Papadogiorgaki¹,

Mezaris²,

Chatzizisis³

et al. 2007

TOBEJ

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Intravascular ultrasound (IVUS) has become in the last years an important tool in both clinical and research applications. The detection of lumen and media-adventitia borders in IVUS images represents a first necessary step in the utilization of the IVUS data for the 3D reconstruction of human coronary arteries and the reliable quantitative assessment of the atherosclerotic lesions. To serve this goal, a fully automated technique for the detection of lumen and mediaadventitia boundaries has been developed. This comprises two different steps for contour initialization, one for each corresponding contour of interest, based on the results of texture analysis, and a procedure for approximating the initialization results with smooth continuous curves. A multilevel Discrete Wavelet Frames decomposition is used for texture analysis, whereas Radial Basis Function approximation is employed for producing smooth contours. The proposed method shows promising results compared to a previous approach for texture-based IVUS image analysis.

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“…As a machine learning approach, in [11], a Hopfield neural network is used to modify and minimize the energy function; in [24], data sets are used to learn what pixels the human observer most often chooses for border pixels, then uses the learned system to detect border.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Segmentation for Determination of IV Vessel Boundaries

Tayel¹,

Massoud²,

Shehata³

2014

IJBBB

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Abstract-Coronary heart disease ranks as the top disease which causes the death in the Egypt. Intravascular (IV) ultrasound imaging is used along with X-ray coronary angiography to detect vessel pathologies. This paper introduces three automated approaches for detection of lumen and media-adventitia borders in IV images. In the first method, a 2D median filter is used to initialize the lumen and media-adventitia contours. The second method uses the Lucy-Richardson method and the 2D median filter to determine the lumen border. Method three is a combination of two methods to combine their advantages. The catheter boundaries were automatically determined. This method shows a higher accuracy and ability to work under artifacts and noisy conditions.

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An Automated Method for Lumen and Media–Adventitia Border Detection in a Sequence of IVUS Frames

Cited by 90 publications

References 16 publications

Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach

Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach

Texture Analysis and Radial Basis Function Approximation for IVUS Image Segmentation

An Automatic Segmentation for Determination of IV Vessel Boundaries

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