Interactive Segmentation of Media-Adventitia Border in IVUS

Jones, Jonathan-Lee; Essa, Ehab; Xie, Xianghua; Smith, Dave

doi:10.1007/978-3-642-40246-3_58

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…However, only the media-adventitia border was extracted in this system. Jones, Essa [41] proposed a graph based method for segmentation of media-adventitia. A multilayer graph is then constructed with regard to the user selected points.…”

Section: Edge-tracking and Gradient-based Techniques Thresholding Is A Histogram-based Techniquementioning

confidence: 99%

Systematic mapping study on diagnosis of vulnerable plaque

Rezaei

Selamat

Taki

et al. 2019

Multimed Tools Appl

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Post-mortem studies demonstrate that around two thirds of all myocardial infarctions are typically result of the plaque rupture. In this paper, systematic mapping study is applied to specify the vulnerable plaque research area. The scope of this research has been limited to the published papers of IEEE Transactions, Sciencedirect, and Springer between 2000 and 2016 years. The related studies are categorized into research question, research strategy, research challenge, and research framework. Based on the mapping results, the researchers are focused on the clinical analysis and algorithmic approach. This paper describes a review of state-of-the-art literature on TCFA detection techniques, motivations, issues, and existing challenges in terms of imaging modalities, plaque characterization techniques, and plaque type classification. A summary of each study containing the author names, publication year, technique, advantages, and drawbacks is presented at the end of each subsection.

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Section: Edge-tracking and Gradient-based Techniques Thresholding Is A Histogram-based Techniquementioning

confidence: 99%

Systematic mapping study on diagnosis of vulnerable plaque

Rezaei

Selamat

Taki

et al. 2019

Multimed Tools Appl

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show abstract

“…A fully automated segmentation step and a user-guided refinement stage for segmentation of luminal and external elastic lamina surface was presented by Sun et al [33]. Jones et al [34] presented a user-assisted method for media-adventitia border segmentation in IVUS images. To detect the media-adventitia border, fully automatic approaches were presented in [35,36].…”

Section: Related Workmentioning

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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“…Essa et al applied graph cuts for the segmentation of intravascular ultrasound images, where the media‐adventitia border is automatically detected without user intervention, based on the minimization of a cost function derived from features like edge/boundary, shape prior, and texture. The authors in incorporated the edge‐based and region‐based constraints with graph cut and superpixel for interactive segmentation of the media‐adventitia border in intravascular ultrasound images and lumen border in optical coherence tomography images. In , Li et al validated the 3D surface segmentation in medical volumes employing graph cuts, but this process is computationally expensive.…”

Section: Applications Of Energy Minimizationmentioning

confidence: 99%

Energy minimization in medical image analysis: Methodologies and applications

Zhang

Xie

2015

Numer Methods Biomed Eng

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Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree-reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, for example, image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well.

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Interactive Segmentation of Media-Adventitia Border in IVUS

Cited by 7 publications

References 10 publications

Systematic mapping study on diagnosis of vulnerable plaque

Systematic mapping study on diagnosis of vulnerable plaque

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

Energy minimization in medical image analysis: Methodologies and applications

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