Three-dimensional automatic quantitative analysis of intravascular ultrasound images

Kovalski, Gil; Beyar, Rafael; Shofti, Rona; Azhari, Haim

doi:10.1016/s0301-5629(99)00167-2

Cited by 76 publications

(36 citation statements)

References 10 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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“…where percentage difference ranges from zero, for complete agreement, to 100%, for complete disagreement between R 1 and R 2 (14,25,26). The only disadvantage of using nonoverlappingregion analysis is that it is invariant to the spatial shift between the 2 regions; therefore, the difference is also reported in conjunction with DSC to calculate the interobserver variability.…”

Section: Percentage Overlap ðRmentioning

confidence: 99%

Semiautomatic Region-of-Interest Validation at the Femur in 18F-Fluoride PET/CT

Puri

Blake

Curran

et al. 2012

Journal of Nuclear Medicine Technology

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The assessment of regional skeletal metabolism using 18 F-fluoride PET ( 18 F-PET) requires segmentation of the tissue region of interest (ROI). The aim of this study was to validate a novel approach to define multiple ROIs at the proximal femur similar to those used in dual x-ray absorptiometry. Regions were first drawn on low-dose CT images acquired as a routine part of the PET/CT study and transferred to the 18 F-PET images for the quantitative analysis of bone turnover. Methods: Four healthy postmenopausal women with a mean age of 65.1 y (range, 61.8-70.0 y), and with no history of metabolic bone disorder and not currently being administered treatment affecting skeletal metabolism, underwent dynamic 18 F-PET/CT at the hip with an injected activity of 180 MBq. The ROIs at the proximal femur included femoral shaft, femoral neck, and total hip and were segmented using both a semiautomatic method and manually by 8 experts at manual ROI delineation. The mean of the 8 manually drawn ROIs was considered the gold standard against which the performances of the semiautomatic and manual methods were compared in terms of percentage overlap and percentage difference. The time to draw the ROIs was also compared. Results: The percentage overlaps between the gold standard and the semiautomatic ROIs for total hip, femoral neck, and femoral shaft were 86.1%, 37.8%, and 96.1%, respectively, and the percentage differences were 14.5%, 89.7%, and 4.7%, respectively. In the same order, the percentage overlap between the gold standard and the manual ROIs were 85.2%, 39.1%, and 95.2%, respectively, and the percentage differences were 19.9%, 91.6%, and 12.2%, respectively. The semiautomatic method was approximately 9.5, 2.5, and 67 times faster than the manual method for segmenting total-hip, femoral-neck, and femoral-shaft ROIs, respectively. Conclusion: We have developed and validated a semiautomatic procedure whereby ROIs at the hip are defined using the CT component of an 18 F-PET/CT scan. The percentage overlap and percentage difference results between the semiautomatic method and the manual method for ROI delineation were similar. Two advantages of the semiautomatic method are that it is significantly quicker and eliminates some of the variability associated with operator or reader input. The tube current used for the CT scan was associated with an effective dose 8 times lower than that associated with a typical diagnostic CT scan. These results suggest that it is possible to segment bone ROIs from low-dose CT for later transfer to PET in a single PET/CT procedure without the need for an additional high-resolution CT scan. The assessment of regional bone turnover is important in the understanding of the pathophysiology of metabolic bone diseases such as osteoporosis. Studies of the differential response between cortical and trabecular bone to aging, disease, and treatment may provide a better understanding of mechanisms of action of novel treatments currently being developed for osteoporosis (1,2) and allow insight into the rol...

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“…The active contour principles have been used to allow the extraction of the borders in three dimensions after setting an initial contour in Kovalski et al's. approach [5]. However, the contour detection fails for low contrast interface regions such as the luminal border where the blood-wall interface in most images corresponds to weak pixel intensity variation.…”

Section: Related Workmentioning

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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Three-dimensional automatic quantitative analysis of intravascular ultrasound images

Cited by 76 publications

References 10 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

Semiautomatic Region-of-Interest Validation at the Femur in 18F-Fluoride PET/CT

Texture Analysis and Radial Basis Function Approximation for IVUS Image Segmentation

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