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The curvelet transform, which represents images in terms of their geometric and textural characteristics, was investigated toward revealing differences between moderate (50%-69%, n = 11) and severe (70%-100%, n = 14) stenosis asymptomatic plaque from B-mode ultrasound. Texture features were estimated in original and curvelet transformed images of atheromatous plaque (PL), the adjacent arterial wall (intima-media [IM]) and the plaque shoulder (SH) (i.e., the boundary between plaque and wall), separately at end systole and end diastole. Seventeen features derived from the original images were significantly different between the two groups (4 for IM, 3 for PL and 10 for SH; 9 for end diastole and 8 for end systole); 19 of 234 features (2 for IM and 17 for SH; 8 for end systole and 11 for end diastole) derived from curvelet transformed images were significantly higher in the patients with severe stenosis, indicating higher magnitude, variation and randomness of image gray levels. In these patients, lower body height and higher serum creatinine concentration were observed. Our findings suggest that (a) moderate and severe plaque have similar curvelet-based texture properties, and (b) IM and SH provide useful information about arterial wall pathophysiology, complementary to PL itself. The curvelet transform is promising for identifying novel indices of cardiovascular risk and warrants further investigation in larger cohorts., (Copyright © 2021 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Carotid atherosclerosis is the major cause of ischemic stroke resulting in significant rates of mortality and disability annually. Early diagnosis of such cases is of great importance, since it enables clinicians to apply a more effective treatment strategy. This paper introduces an interpretable classification approach of carotid ultrasound images for the risk assessment and stratification of patients with carotid atheromatous plaque. To address the highly imbalanced distribution of patients between the symptomatic and asymptomatic classes (16 vs 58, respectively), an ensemble learning scheme based on a sub-sampling approach was applied along with a two-phase, cost-sensitive strategy of learning, that uses the original and a resampled data set. Convolutional Neural Networks (CNNs) were utilized for building the primary models of the ensemble. A six-layer deep CNN was used to automatically extract features from the images, followed by a classification stage of two fully connected layers. The obtained results (Area Under the ROC Curve (AUC): 73%, sensitivity: 75%, specificity: 70%) indicate that the proposed approach achieved acceptable discrimination performance. Finally, interpretability methods were applied on the model's predictions in order to reveal insights on the model's decision process as well as to enable the identification of novel image biomarkers for the stratification of patients with carotid atheromatous plaque.Clinical Relevance-The integration of interpretability methods with deep learning strategies can facilitate the identification of novel ultrasound image biomarkers for the stratification of patients with carotid atheromatous plaque.

Stroke is a leading cause of morbidity and mortality worldwide, and characterization of vulnerable carotid plaque remains the spearhead of scientific research. Plaque destabilization, the key factor that induces the series of events leading to the clinical symptoms of carotid artery disease, is a consequence of complex mechanical, structural and biochemical processes. Novel imaging and molecular markers have been studied as predictors of disease outcome with promising results. The aim of this review is to present the current state of research on the association between ultrasound-derived echogenicity indices and blood parameters indicative of carotid plaque stability and activity. Bibliographic research revealed that there are limited available data. Among the biomarkers studied, those related to oxidative stress, lipoproteins and diabetes/insulin resistance are associated with echolucent plaques, whereas adipokines are associated with echogenic plaques. Biomarkers of inflammation and coagulation have not exhibited any conclusive relationship with plaque echogenicity, and it is not possible to come to any conclusion regarding calcification-, apoptosis- and neo-angiogenesis-related parameters because of the extremely limited bibliographic data., (Copyright © 2016. Published by Elsevier Inc.)

Valid characterization of carotid atherosclerosis (CA) is a crucial public health issue, which would limit the major risks held by CA for both patient safety and state economies. This paper investigated the unexplored potential of kinematic features in assisting the diagnostic decision for CA in the framework of a computer-aided diagnosis (CAD) tool. To this end, 15 CAD schemes were designed and were fed with a wide variety of kinematic features of the atherosclerotic plaque and the arterial wall adjacent to the plaque for 56 patients from two different hospitals. The CAD schemes were benchmarked in terms of their ability to discriminate between symptomatic and asymptomatic patients and the combination of the Fisher discriminant ratio, as a feature-selection strategy, and support vector machines, in the classification module, was revealed as the optimal motion-based CAD tool. The particular CAD tool was evaluated with several cross-validation strategies and yielded higher than 88% classification accuracy; the texture-based CAD performance in the same dataset was 80%. The incorporation of kinematic features of the arterial wall in CAD seems to have a particularly favorable impact on the performance of image-data-driven diagnosis for CA, which remains to be further elucidated in future prospective studies on large datasets.

Valid characterization of carotid atherosclerosis (CA) is a crucial public health issue, which would limit the major risk held by CA for both patient safety and state economies. CA is typically diagnosed and assessed using duplex ultrasonography (US). Elastrography Imaging (EI) is a promising US technique for quantifying tissue elasticity (ES). In this work, we investigated the association between ES of carotid atherosclerotic lesions, derived from EI, and texture indices, calculated from US image analysis. US and EI images of 23 atherosclerotic plaques (16 patients) were analyzed. Texture features derived from US image analysis (Gray-Scale Median (GSM), plaque area (A) and co-occurrence-matrixderived features) were calculated. Statistical analysis revealed associations between US texture features and EI measured indices. This result indicates accordance in US and EI techniques and states the promising role of EI in diagnosis of CA.

Carotid atherosclerosis is the main cause of fatal cerebral ischemic events, thereby posing a major burden for public health and state economies. We propose a web-based platform named CAROTID to address the need for optimal management of patients with carotid atherosclerosis in a twofold sense: (a) objective selection of patients who need carotid-revascularization (i.e., high-risk patients), using a multifaceted description of the disease consisting of ultrasound imaging, biochemical and clinical markers, and (b) effective storage and retrieval of patient data to facilitate frequent follow-ups and direct comparisons with related cases. These two services are achieved by two interconnected modules, namely the computer-aided diagnosis (CAD) tool and the intelligent archival system, in a unified, remotely accessible system. We present the design of the platform and we describe three main usage scenarios to demonstrate the CAROTID utilization in clinical practice. Additionally, the platform was evaluated in a real clinical environment in terms of CAD performance, end-user satisfaction and time spent on different functionalities. CAROTID classification of high- and low-risk cases was 87%; the corresponding stenosis-degree-based classification would have been 61%. Questionnaire-based user satisfaction showed encouraging results in terms of ease-of-use, clinical usefulness and patient data protection. Times for different CAROTID functionalities were generally short; as an example, the time spent for generating the diagnostic decision was 5min in case of 4-s ultrasound video. Large datasets and future evaluation sessions in multiple medical institutions are still necessary to reveal with confidence the full potential of the platform., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

Stroke is a serious and frequent cerebrovascular disease with an enormous socioeconomic burden worldwide. Stroke prevention includes treatment of carotid atherosclerosis, the most common underlying cause of stroke, according to a specific diagnostic algorithm. However, this diagnostic algorithm has proved insufficient for a large number of mostly asymptomatic subjects, which poses a significant research challenge of identifying novel personalized risk markers for the disease. This paper illustrates the potential of carotid ultrasound image analysis toward this direction, with ultrasound imaging being a low-cost and noninvasive imaging modality and ultrasound-image-based features revealing valuable information on plaque composition and stability. A concise report of state-of-the-art studies in the field is provided and a perspective for clinical scenario for optimal management of atherosclerotic patients is described. Challenges and necessary future steps toward the realization of this scenario are discussed in an attempt to urge and orient future research, and mainly include systematic applications to sufficiently large patient samples, appropriately designed longitudinal studies, confirmation with histological results, and clinical trials.

In this paper, a multiresolution approach is suggested for texture classification of atherosclerotic tissue from B-mode ultrasound. Four decomposition schemes, namely, the discrete wavelet transform, the stationary wavelet transform, wavelet packets (WP), and Gabor transform (GT), as well as several basis functions, were investigated in terms of their ability to discriminate between symptomatic and asymptomatic cases. The mean and standard deviation of the detail subimages produced for each decomposition scheme were used as texture features. Feature selection included 1) ranking the features in terms of their divergence values and 2) appropriately thresholding by a nonlinear correlation coefficient. The selected features were subsequently input into two classifiers using support vector machines (SVM) and probabilistic neural networks. WP analysis and the coiflet 1 produced the highest overall classification performance (90% for diastole and 75% for systole) using SVM. This might reflect WP's ability to reveal differences in different frequency bands, and therefore, characterize efficiently the atheromatous tissue. An interesting finding was that the dominant texture features exhibited horizontal directionality, suggesting that texture analysis may be affected by biomechanical factors (plaque strains).

Texture analysis of B-mode ultrasound images of carotid atheromatous plaque can be valuable for the accurate diagnosis of atherosclerosis. In this paper, Gabor filters were used to characterize the texture of carotid artery atherosclerotic tissue. B-mode ultrasound images of 10 symptomatic and 9 asymptomatic plaques were interrogated. A total of 40 texture features were estimated for each plaque. The bootstrap method was used to compare the mean values of the texture features extracted from the two groups. After bootstrapping, the mean value and the standard deviation of the energy estimated using the Gabor filters was found to be significantly different between symptomatic and asymptomatic plaques in the first scale of analysis and for all orientations. In addition, a number of texture features that correspond to larger resolution scales were found to be significantly different between the two types of plaques. It is concluded that Gabor-filter-based texture analysis in combination with a powerful statistical technique, such as bootstrapping, may provide valuable information about the plaque tissue type.

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

Objective: The echogenicity of the carotid atheromatous plaque estimated from B-mode sonographic images may be related to plaque content and has been analyzed with a number of techniques. The purpose of this study was to compare plaque with surrounding muscle tissue echogenicity and to validate the use of first-order mathematical descriptors as determinants of tissue echogenicity., Methods: We estimated echogenicity descriptors for regions in the following 3 distinct areas of a typical B-mode sonographic image of a diseased carotid artery: plaque, blood, and sternocleidomastoid muscle. Two-dimensional B-mode sonographic images from 19 symptomatic and asymptomatic plaques (17 subjects) were interrogated, in which the following estimators were calculated: minimal, maximal, mean, and median gray levels, SD of gray levels, coefficient of variation, and gray level skewness, kurtosis, entropy, and energy. Plaque echogenicity was estimated at systole and diastole for studying the effect of the phase of the cardiac cycle. To assess the effect of different fascial tissue-muscle fiber composition on the first-order mathematical descriptors, we estimated echogenicity at 3 different sites within the muscle tissue., Results: Compared with the echogenicity of surrounding muscle tissue, plaque echogenicity was characterized by (1) significantly lower (Student t test, P < .05) gray level mean, median, SD, and entropy; (2) significantly higher (Student t test, P < .05) coefficient of variation, gray level skewness, and kurtosis; and (3) similar minimal and maximal gray levels and gray level energy. The phase of the cardiac cycle, systole or diastole, did not affect the estimation of plaque echogenicity. Echogenicity was found to vary within the muscle tissue. The coefficient of variation was significantly higher in the asymptomatic plaques in the small group that was investigated., Conclusions: First-order statistical descriptors may be used to characterize atheromatous plaques and the surrounding muscle tissue.

The motion of the carotid atheromatous plaque relative to the adjacent wall may be related to the risk of cerebral events. A quantitative method for motion estimation was applied to analyse arterial wall movement from sequences of 2-D B-mode ultrasound (US) images. Image speckle patterns were tracked between successive frames using the correlation coefficient as the matching criterion. The size of the selected region-of-interest (ROI) was shown to affect the motion analysis results; an optimal size of 3.2 x 2.5 mm(2) was suggested for tracking a region at the wall-lumen interface and of 6.3 x 2.5 mm(2) for one within the tissue. The results showed expected cyclical motion in the radial direction and some axial movement of the arterial wall. The method can be used to study further the axial motion of the carotid artery wall and plaque and, thus, provide useful insight into the mechanisms of atherosclerosis.

In this paper, a pilot study regarding carotid atherosclerotic plaque instability using B-mode ultrasound (US) images was carried out. The fractal dimension of plaques obtained from ten symptomatic subjects (i.e., subjects having experienced neurological symptoms) as well as from nine asymptomatic subjects, was estimated using a novel method, called the kth nearest neighbour (KNN) method. The results indicated a significant difference, as per the fractal dimension, between the two groups, providing a significantly lower value for the asymptomatic group. Moreover, the phase of the cardiac cycle (systole/diastole) during which the fractal dimension was estimated had no systematic effect on the calculations. The fractal dimension of the plaques was also estimated using a well-known method, namely the box-counting (BC) method. No significant differences between the two groups, as per the fractal dimension, were observed using the BC method. The presented pilot study suggests that the fractal dimension, estimated by the proposed method, could be used as a single determinant for the discrimination of symptomatic and asymptomatic subjects., (Copyright 2002 World Federation for Ultrasound in Medicine & Biology)

Motion of the carotid artery wall is important for the quantification of arterial elasticity and contractility and can be estimated with a number of techniques. In this paper, a framework for quantitative evaluation of motion analysis techniques from B-mode ultrasound images is introduced. Six synthetic sequences were produced using 1) a real image corrupted by Gaussian and speckle noise of 25 and 15 dB, and 2) the ultrasound simulation package Field II. In both cases, a mathematical model was used, which simulated the motion of the arterial wall layers and the surrounding tissue, in the radial and longitudinal directions. The performance of four techniques, namely optical flow (OFHS), weighted least-squares optical flow (OF LK(WLS)), block matching (BM), and affine block motion model (ABMM), was investigated in the context of this framework. The average warping indices were lowest for OF LK(WLS) (1.75 pixels), slightly higher for ABMM (2.01 pixels), and highest for BM (6.57 pixels) and OFHS (11.57 pixels). Due to its superior performance, OFLK(WLS) was used to quantify motion of selected regions of the arterial wall in real ultrasound image sequences of the carotid artery. Preliminary results indicate that OFLK(WLS) is promising, because it efficiently quantified radial, longitudinal, and shear strains in healthy adults and diseased subjects. [ABSTRACT FROM PUBLISHER]

Purpose: The purpose of this study was to investigate differences in ultrasound-based velocities and accelerations of the carotid atheromatous plaque between asymptomatic patients with moderate and severe stenosis, based on the assumption that plaque motion features are sensitive to cardiovascular health status. Methods: The dataset used consists of 38 sequences of B-mode images (videos) of carotid atheromatous plaque of asymptomatic patients. Among the examined carotid arteries, 27 had severe stenosis degrees (>70%) and 11 had moderate stenosis degrees (<70%). Plaque motion estimation was based on an adaptive block matching methodology which incorporates Kalman filtering update strategies. Plaque velocity and acceleration were calculated by differentiating displacement and velocity, respectively. The two-tailed t-test was used to assess statistically significant differences. Results: Figure shows examples of plaque velocity and acceleration waveforms. Radial and longitudinal plaque velocities were similar between moderate and severe cases (0.43 ± 0.15 mm/s and 0.73 ± 0.18 mm/s, respectively, vs. 0.37 ± 0.21 mm/s and 0.65 ± 0.20 mm/s, respectively; p-values 0.35 and 0.27, respectively). Radial and longitudinal plaque accelerations were significantly higher in moderate compared to severe stenosis cases (7.07 ± 4.64 mm/s2 and 10.08 ± 5.22 mm/s2, respectively, vs. 3.17 ± 2.25 mm/s2 and 5.05 ± 2.96 mm/s2, respectively; p-values = 0.00 for both cases). In moderate stenoses, longitudinal velocities, but not accelerations, were significantly higher than radial ones, whereas in severe stenoses, longitudinal velocities and accelerations were significantly higher than radial ones. Conclusion: Ultrasound-based velocities and accelerations can characterise biomechanical phenomena of the carotid plaque. Accelerations can differentiate between moderate and severe plaque. [ABSTRACT FROM AUTHOR]