Automatic plaque characterization and vessel wall segmentation in magnetic resonance images of atherosclerotic carotid arteries

Adame, Isabel M.; Geest, Rob J. van der; Wasserman, Bruce A.; Mohamed, Mona A.; Reiber, J. H. C.; Lelieveldt, Boudewijn P. F.

doi:10.1117/12.532471

Cited by 10 publications

(9 citation statements)

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“…So far, the vascular abnormality detection methods reported in literature [1,6,16,[18][19][20] have been directed toward pathology modeling, i.e. identifying the particularities of anomalies.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines

et al. 2010

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Section: Introductionmentioning

confidence: 99%

Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines

et al. 2010

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“…In addition, calcified plaques could harm the segmentation due to projected shadow that can precludes regions of interest (Molinari et al, 2010). lumen is also covered in another study of Adame et al (2004a).…”

Section: Discussionmentioning

confidence: 99%

A review of computational methods applied for identification and quantification of atherosclerotic plaques in images

Jodas

Pereira

Tavares

2016

Expert Systems with Applications

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Evaluation of the composition of atherosclerotic plaques in images is an important task to determine their pathophysiology. Visual analysis is still as the most basic and often approach to determine the morphology of the atherosclerotic plaques. In addition, computer-aided methods have also been developed for identification of features such as echogenicity, texture and surface in such plaques. In this article, a review of the most important methodologies that have been developed to identify the main components of atherosclerotic plaques in images is presented. Hence, computational algorithms that take into consideration the analysis of the plaques echogenicity, image processing techniques, clustering algorithms and supervised classification used for segmentation, i.e. identification, of the atherosclerotic plaque components in ultrasound, computerized tomography and magnetic resonance images are introduced. The main contribution of this paper is to provide a categorization of the most important studies related to the segmentation of atherosclerotic plaques and its compo- * Corresponding author. Tel.: +351 22 508 1487; fax: +351 22 508 1445Email addresses: danilojodas@gmail.com (Danilo Samuel Jodas), aledir@sjrp.unesp.br (Aledir Silveira Pereira), tavares@fe.up.pt (João Manuel R. S. Tavares ) URL: http://www.fe.up.pt/~tavares (João Manuel R. S. Tavares ) October 12, 2015 nents in images acquired by the most used imaging modalities. In addition, the effectiveness and drawbacks of each methodology as well as future researches concerning the segmentation and classification of the atherosclerotic lesions are also discussed. Preprint submitted to Journal of L A T E X Templates

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“…The semiautomatic segmentation of the lumen and wall of the carotid artery was tackled by Adame et al [22,23]. In those studies, ellipse fitting was used to detect the vessel wall boundary, while fuzzy clustering was applied to identify the lumen and carotid plaques.…”

Section: Introductionmentioning

confidence: 99%

Lumen segmentation in magnetic resonance images of the carotid artery

Jodas

Pereira

Tavares

2016

Computers in Biology and Medicine

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Investigation of the carotid artery plays an important role in the diagnosis of cerebrovascular events. Segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images is the first step towards evaluating any possible cardiovascular diseases like atherosclerosis. However, the automatic segmentation of the lumen is still a challenge due to the low quality of the images and the presence of other elements such as stenosis and malformations that compromise the accuracy of the results. In this article, a method to identify the location of the lumen without user interaction is presented. The proposed method uses the modified mean roundness to calculate the circularity index of the regions identified by the K-means algorithm and return the one with the maximum value, i.e. the potential lumen region. Then, an active contour is employed to refine the boundary of this region. The method achieved an average Dice coefficient of 0.78±0.14 and 0.61±0.21 in 181 3D-T1-weighted and 181 proton density-weighted MR images, respectively. The results show that this method is promising for the correct identification and location of the lumen even in images corrupted by noise.

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Automatic plaque characterization and vessel wall segmentation in magnetic resonance images of atherosclerotic carotid arteries

Cited by 10 publications

References 0 publications

Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines

Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines

A review of computational methods applied for identification and quantification of atherosclerotic plaques in images

Lumen segmentation in magnetic resonance images of the carotid artery

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