An approach to the classification, diagnosis and management of vulnerable plaque

Mishra, Trinath Kumar; Mishra, Chandrakant; Das, Biswajit

doi:10.1016/j.jicc.2013.03.007

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…Spectral overlapping of NC with DC makes the segmentation process difficult [22]. Accurate segmentation of DC and NC are also required, since when DC is next to the NC in IVUS images, DC is often coded as NC [50]. The existing technology for VH-IVUS image segmentation is based on simple thresholding approaches.…”

Section: Segmentationmentioning

confidence: 99%

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

et al. 2018

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Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.

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Section: Segmentationmentioning

confidence: 99%

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

et al. 2018

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“…The vulnerable plaques are those more prone to rupture and thrombosis [16]. From a histological point of view, the vulnerable plaques are usually characterized by the presence of a large fatty necrotic core, covered by a very thin fibrous cap [17].…”

Section: Types Of Plaque and Possible Complicationsmentioning

confidence: 99%

“…From a histological point of view, the vulnerable plaques are usually characterized by the presence of a large fatty necrotic core, covered by a very thin fibrous cap [17]. The cap consists of molecules (in particular collagen, elastin, and proteoglycans) produced largely by the VSMC and protects the core of the plaque from the direct contact with the blood [16]. The necrotic lipid core is rich in prothrombotic molecules; after the fissuring of the plaque, the necrotic lipid core is exposed to the blood flow, unleashing the coagulation cascade and the formation of thrombus; the thrombus can completely occlude the vessel determining ischemia and then infarction of the tissues supplied by the vessels and/or can shatter and embolize peripherally.…”

Section: Types Of Plaque and Possible Complicationsmentioning

confidence: 99%

Atherosclerosis, Biological and Pathological Basis

Porcu¹,

Raz²,

Saba³

2015

Neurovascular Imaging

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Atherosclerosis is a extremely complicated and still well not perfectly understood pathologic process that involves the arteries of the human body. It is a multi-step process that starts with the endothelial damage and can be influenced by many factors, modifiable or not. The definition of the grade of stenosis determined by an atherosclerotic plaque and the identification of vulnerable plaques are the main goals for diagnosis, staging and therapies.

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“…The treatment of ulcerated plaques is an open discussion, due to the high geometry heterogeneity, depth and extension, and the lack of indications that support particular strategies [6]. Stent deployment is suggested for plaques with relevant degree of lumen narrowing (70-90%) and in association with unstable conditions.…”

Section: Introductionmentioning

confidence: 99%

Application of an OCT-based 3D reconstruction framework to the hemodynamic assessment of an ulcerated coronary artery plaque

Migliori

Chiastra

Bologna

et al. 2020

Medical Engineering & Physics

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An approach to the classification, diagnosis and management of vulnerable plaque

Cited by 7 publications

References 32 publications

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

Atherosclerosis, Biological and Pathological Basis

Application of an OCT-based 3D reconstruction framework to the hemodynamic assessment of an ulcerated coronary artery plaque

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