Stroke Due to Large Artery Atherosclerosis

Furie, Karen L.; Smimakis, Stelios M.; Koroshetz, Walter J.; Kistler, J P

doi:10.1007/978-1-59259-769-7_10

Cited by 3 publications

(1 citation statement)

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“…The atherosclerosis reduces or occludes the blood flow through the artery, which can cause amaurosis fugax, transient ischemic attack and strokes (Furie et al, 2004;Wiebers et al, 2006;Schadé, 2006). and ultrasound are examples of less invasive procedures that have been widely used for evaluating the presence and characteristics of atherosclerotic plaques (Widder et al, 1990;de Weert et al, 2006;Serfaty et al, 2001).…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery

2017

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The segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images of carotid arteries represents a crucial step towards the evaluation of cerebrovascular diseases. However, the automatic segmentation of the lumen is still difficult due to the usual low quality of the images and the presence of elements that might affect the accuracy of the results. In this article, we describe a fully automatic method to identify the location of the lumen in MR images of the carotid artery. A circularity index is used to assess the roundness of the regions identified by the K-means algorithm in order to obtain the one with the maximum value, which represents the potential lumen region. The boundary of the identified lumen region is then refined by an active contour algorithm. The proposed method achieved a maximum Dice coefficient of 0.91 ± 0.04 and 0.74 ± 0.16 in 181 postcontrast 3D-T1-weighted and 181 proton density-weighted MR images, respectively. Therefore, the method seems to be promising for identifying the correct location of the lumen in MR images.

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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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Stroke Due to Large Artery Atherosclerosis

Cited by 3 publications

References 70 publications

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

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

Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery

Lumen segmentation in magnetic resonance images of the carotid artery

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