An IVUS image-based approach for improvement of coronary plaque characterization

Taki, Arash; Roodaki, Alireza; Setarehdan, Seyed Kamaledin; Avansari, Sara; Ünal, Gözde; Navab, Nassir

doi:10.1016/j.compbiomed.2012.12.008

Cited by 23 publications

(17 citation statements)

References 26 publications

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“…The dataset used in this research includes 588 grey-scale IVUS images and their corresponding VH-IVUS images acquired from 10 patients (age, sex and risk factors should be determined) that were used by the authors in [20]. IVUS images of 10 patients (size 400 × 400 pixels) were acquired for VH-IVUS analysis by an electronic probe (In Vision Gold, Volcano Therapeutics Inc.) with a synthetic aperture of 2.9 f and a frequency of 20 MHz.…”

Section: Cluster Validitymentioning

confidence: 99%

“…Based on previous studies, texture analysis is useful to measure the plaque compositions in ultrasound images. Due to the varying texture patterns of plaque components, texture descriptors have been applied for analysis of plaque compositions [20]. When considering statistical texture-based methods, Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run Length Matrix (GLRLM) descriptors remain among the top choices [21].…”

Section: Introductionmentioning

confidence: 99%

“…Katouzian et al [26] presented a texture-based method by applying discrete wavelet packet frame (DWPF) for labelling the atherosclerotic plaque. Taki, Roodaki, Setarehdan, Avansari, Unal and Navab [20] proposed an image-based method for characterisation of coronary plaques in IVUS images. A combination of grey-scale-based features and textural descriptors, including LBP, MRL, and Neighbouring Grey-Level (NGL) are extracted to categorise each pixel into one of the three classes (DC, NC, and FF).…”

Section: Introductionmentioning

confidence: 99%

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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: Cluster Validitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…Although the RF signal provides additional information compared with grayscale IVUS data, VH-IVUS regards the shadow region and other regions in a similar way. (12) Shadow regions usually occur behind DC regions without providing useful information for plaque characterization. Nevertheless, VH-IVUS classifies acoustic shadow regions as FFT or NC class, although they should be assigned as NC or DC.…”

Section: Introductionmentioning

confidence: 99%

“…Only a few studies have attempted to characterize DC and acoustic shadow regions simultaneously from sequential IVUS image frames. (6,12,15) Therefore, the purpose of this study was to use a dual-threshold-based segmentation approach to automatically detect DC and acoustic shadow regions in IVUS images. For this purpose, a total of three hundred IVUS and corresponding VH-IVUS images were acquired from 26 cardiovascular patients.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Dense Calcium and Acoustic Shadow in Intravascular Ultrasound Images by Dual-threshold-based Segmentation Approach

Lee¹,

Kim²,

Hwang³

et al. 2018

Sensors and Materials

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The purpose of this study was to automatically detect dense calcium (DC) and acoustic shadow regions in intravascular ultrasound (IVUS) images by a dual-threshold-based segmentation approach. Three hundred grayscale IVUS and corresponding virtual histology (VH)-IVUS images of human coronary arteries were obtained using a 20 MHz commercial catheter. Plaque regions between intima and media-adventitial borders were manually extracted from all IVUS images. To detect DC and acoustic shadow regions automatically, DC candidates were first selected from plaque regions on the basis of intensity. The shadow mask of each DC candidate was then obtained by calculating its centroid. A DC candidate involving acoustic shadow was finally selected as DC tissue. The segmentation performance of the proposed approach was quantitatively evaluated using the area difference, DC ratio, Hausdorff distance, and Dice similarity coefficient. Quantitative results indicated that all the parameters for the proposed approach were highly similar to those of VH-IVUS. Despite the relatively low agreement (64.1%) for the DC tissue, reliable performance was found for the proposed approach. These experimental results suggest that the proposed method has clinical applicability for diagnosing cardiovascular diseases in IVUS images.

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Atherosclerotic Plaque Detection Using Intravascular Ultrasound (IVUS) Images

Priya

Vanithamani

2019

Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

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An IVUS image-based approach for improvement of coronary plaque characterization

Cited by 23 publications

References 26 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

Automatic Detection of Dense Calcium and Acoustic Shadow in Intravascular Ultrasound Images by Dual-threshold-based Segmentation Approach

Atherosclerotic Plaque Detection Using Intravascular Ultrasound (IVUS) Images

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