Automatic detection of the carotid lumen axis in B-mode ultrasound images

Rocha, Rui; Silva, Jorge; Campilho, Aurélio

doi:10.1016/j.cmpb.2014.04.004

Cited by 15 publications

(17 citation statements)

References 12 publications

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“…The method proposed in [79] was also later applied in [80] for the automated detection of the lumen in CCA B-mode images using a Gaussian smoothed filter and then a dynamic programming scheme, which extracts the dominant paths of local minima of the intensity and the dominant paths of local maxima of the gradient magnitude with the gradient pointing downwards. Similar results were reported in [79], but with a greater accuracy (99.5 %).…”

Section: Atherosclerotic Carotid Plaque Image Segmentation Techniquesmentioning

confidence: 99%

A review of ultrasound common carotid artery image and video segmentation techniques

Loizou

2014

Med Biol Eng Comput

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The determination of the wall thickness [intima-media thickness (IMT)], the delineation of the atherosclerotic carotid plaque, the measurement of the diameter in the common carotid artery (CCA), as well as the grading of its stenosis are important for the evaluation of the atherosclerosis disease. All these measurements are also considered to be significant markers for the clinical evaluation of the risk of stroke. A number of CCA segmentation techniques have been proposed in the last few years either for the segmentation of the intima-media complex (IMC), the lumen of the CCA, or for the atherosclerotic carotid plaque from ultrasound images or videos of the CCA. The present review study proposes and discusses the methods and systems introduced so far in the literature for performing automated or semi-automated segmentation in ultrasound images or videos of the CCA. These are based on edge detection, active contours, level sets, dynamic programming, local statistics, Hough transform, statistical modeling, neural networks, and an integration of the above methods. Furthermore, the performance of these systems is evaluated and discussed based on various evaluation metrics. We finally propose the best performing method that can be used for the segmentation of the IMC and the atherosclerotic carotid plaque in ultrasound images and videos. We end the present review study with a discussion of the different image and video CCA segmentation techniques, future perspectives, and further extension of these techniques to ultrasound video segmentation and wall tracking of the CCA. Future work on the segmentation of the CCA will be focused on the development of integrated segmentation systems for the complete segmentation of the CCA as well as the segmentation and motion analysis of the plaque and or the IMC from ultrasound video sequences of the CCA. These systems will improve the evaluation, follow up, and treatment of patients affected by advanced atherosclerosis disease conditions.

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Section: Atherosclerotic Carotid Plaque Image Segmentation Techniquesmentioning

confidence: 99%

A review of ultrasound common carotid artery image and video segmentation techniques

Loizou

2014

Med Biol Eng Comput

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“…However, we have found that some authors pointed out the need for automated measurement of carotid lumen diameter [47][48][49]. The lumen segmentation algorithms have been classified into regional-based vs. boundary-based: region-based [17,18], boundary-based [19][20][21][22]. Here, we describe each of these techniques and their limitations and discuss how they validated the measured parameters.…”

Section: Brief Survey and Benchmarkingmentioning

confidence: 99%

“…Lumen axis detection for carotid artery was discussed by Rocha et al [22] using dynamic programming. They assumed that the CCA is the largest blood vessel nearest to the bottom of the image and the jugular vein will appear above the CCA.…”

Section: Brief Survey and Benchmarkingmentioning

confidence: 99%

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Two Automated Techniques for Carotid Lumen Diameter Measurement: Regional versus Boundary Approaches

Araki

Kumar

Suri³

et al. 2016

J Med Syst

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The degree of stenosis in the carotid artery can be predicted using automated carotid lumen diameter (LD) measured from B-mode ultrasound images. Systolic velocity-based methods for measurement of LD are subjective. With the advancement of high resolution imaging, image-based methods have started to emerge. However, they require robust image analysis for accurate LD measurement. This paper presents two different algorithms for automated segmentation of the lumen borders in carotid ultrasound images. Both algorithms are modeled as a two stage process. Stage one consists of a global-based model using scale-space framework for the extraction of the region of interest. This stage is common to both algorithms. Stage two is modeled using a local-based strategy that extracts the lumen interfaces. At this stage, the algorithm-1 is modeled as a region-based strategy using a classification framework, whereas the algorithm-2 is modeled as a boundary-based approach that uses the level set framework. Two sets of databases (DB), Japan DB (JDB) (202 patients, 404 images) and Hong Kong DB (HKDB) (50 patients, 300 images) were used in this study. Two trained neuroradiologists performed manual LD tracings. The mean automated LD measured was 6.35 ± 0.95 mm for JDB and 6.20 ± 1.35 mm for HKDB. The precision-of-merit was: 97.4 % and 98.0 % w.r.t to two manual tracings for JDB and 99.7 % and 97.9 % w.r.t to two manual tracings for HKDB. Statistical tests such as ANOVA, Chi-Squared, T-test, and Mann-Whitney test were conducted to show the stability and reliability of the automated techniques.

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“…However, the method might result in false CCA detections due to confusion with other structures of similar shape. In a recent paper, Rocha et al [ 19 ] presented an independently-developed approach to CCA detection that proposes a solution for the detection of multiple candidates.…”

Section: Introductionmentioning

confidence: 99%

Automatic Lumen Detection on Longitudinal Ultrasound B-Mode Images of the Carotid Using Phase Symmetry

Rouco

Azevedo

Campilho

2016

Sensors

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This article describes a method that improves the performance of previous approaches for the automatic detection of the common carotid artery (CCA) lumen centerline on longitudinal B-mode ultrasound images. We propose to detect several lumen centerline candidates using local symmetry analysis based on local phase information of dark structures at an appropriate scale. These candidates are analyzed with selection mechanisms that use symmetry, contrast or intensity features in combination with position-based heuristics. Several experimental results are provided to evaluate the robustness and performance of the proposed method in comparison with previous approaches. These results lead to the conclusion that our proposal is robust to noise, lumen artifacts, contrast variations and that is able to deal with the presence of CCA-like structures, significantly improving the performance of our previous approach, from 87.5%±0.7% of correct detections to 98.3%±0.3% in a set of 200 images.

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Automatic detection of the carotid lumen axis in B-mode ultrasound images

Cited by 15 publications

References 12 publications

A review of ultrasound common carotid artery image and video segmentation techniques

A review of ultrasound common carotid artery image and video segmentation techniques

Two Automated Techniques for Carotid Lumen Diameter Measurement: Regional versus Boundary Approaches

Automatic Lumen Detection on Longitudinal Ultrasound B-Mode Images of the Carotid Using Phase Symmetry

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