Nikolaos N. Tsiaparas

Assessment of carotid atherosclerosis from B-mode ultrasound images using directional multiscale texture features

In this paper, three multiscale transforms with directional character, namely the dual-tree complex wavelet (DTCWT), the finite ridgelet (FRIT) and the fast discrete curvelet (FDCT) transforms, were comparatively assessed with respect to their ability to characterize carotid atherosclerotic plaque from B-mode ultrasound and discriminate between symptomatic and asymptomatic cases. The standard deviation and entropy of the detail subimages produced for each decomposition scheme were used as texture features. Feature selection included ranking the features according to their highest separability value and the minimum correlation among them. Due to the rather limited size of the sample population, the selected features were resampled 100 times by the bootstrap technique and divided into training and test sets. For each pair of sets, a support vector machine classifier was trained on the training set and evaluated on the test set. The average overall classification performance for systole (diastole) was 70% (65.2%), 72.6% (70.4%) and 84.9% (73.6%) for the DTCWT, FRIT and FDCT, respectively. These preliminary results showed the superiority of the curvelet transform, in terms of classification accuracy, being of great importance for the diagnosis and management of plaque instability in carotid atheromatous stenosis.

CAROTID - A web-based platform for optimal personalized management of atherosclerotic patients

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.

Characterization of carotid atherosclerotic plaques using frequency-based texture analysis and bootstrap.

Texture analysis of B-mode ultrasound images of carotid atheromatous plaque can be valuable for the accurate diagnosis of atherosclerosis. In this paper, two frequency-based texture analysis methods based on the Fourier Power Spectrum and the Wavelet Transform were used to characterize atheromatous plaques. B-mode ultrasound images of 10 symptomatic and 9 asymptomatic plaques were interrogated. A total of 109 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, three features were found to be significantly different between the two types of plaques: the average value of the angular distribution corresponding to the wedge centered at 90deg, the standard deviation at scale 1 derived from the horizontal detail image, and the standard deviation at scale 2 derived from the horizontal detail image. It is concluded that frequency-based texture analysis in combination with a powerful statistical technique, such as bootstrapping, may provide valuable information about the plaque tissue type

Texture characterization of carotid atherosclerotic plaque from B-mode ultrasound using gabor filters

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.

Discrete wavelet transform vs. wavelet packets for texture analysis of ultrasound images of carotid atherosclerosis

In this paper, a scale/frequency approach, based on the wavelet transform, was used in an attempt to characterize carotid atherosclerotic plaque from B-mode ultrasound. Two wavelet decomposition schemes, namely the discrete wavelet transform (DWT) and wavelet packets (WP), and three basis functions, namely Haar, symlet3 and biorthogonal3.1, were investigated in terms of their ability to discriminate between symptomatic and asymptomatic cases. A total of 12 detail sub-images were extracted using the DWT and 255 using the WP decomposition schemes. It was shown that WP analysis by the use of Haar filter and the l-1 norm as texture descriptor could reveal differences not only in high but also in low frequencies, and therefore characterize efficiently the atheromatous tissue. Additional studies applying and further extending the above methodology are required to ensure the usefulness of wavelet-based texture analysis of carotid atherosclerosis.

Affine optical flow combined with multiscale image analysis for motion estimation of the arterial wall from B-mode ultrasound

This paper investigated the performance of affine optical flow (AFOF) in motion tracking of the arterial wall from B-mode ultrasound images and the effect of its combination with multiscale image analysis on the accuracy of the process. Multiscale AFOF (MAFOF) exploits the information obtained with AFOF from the approximation sub-images at different spatial resolution levels of the images, obtained using a 2D discrete wavelet transform. Both AFOF and MAFOF were evaluated through their application to synthetic image sequences of the common carotid artery. Multiscale image analysis increased the accuracy in motion tracking, with MAFOF yielding average displacement error reductions of 9% with respect to AFOF. The methods were also effectively applied to real ultrasound image sequences of the carotid artery. The results showed that MAFOF could be considered as a reliable estimator for arterial wall motion from B-mode ultrasound images.

Toward recognizing the vulnerable asymptomatic atheromatous plaque from B-mode ultrasound: the importance of the morphology of the plaque shoulder

Efficient management of the asymptomatic carotid disease remains a crucial challenge in clinical practice, because the ultrasonographically estimated degree of stenosis, which is currently used to determine treatment decisions, has been shown to be inadequate. In this study, texture (morphological) characteristics were investigated in a sample of asymptomatic male subjects, at the atheromatous plaque, the adjacent arterial wall and the plaque shoulder, i.e. the boundary between plaque and adjacent wall. A total of 25 arteries were interrogated, 11 with low (50-69%) and 14 with high (70-100%) degrees of stenosis. The two groups had similar ages. Texture characteristics were estimated from systolic and diastolic B-mode ultrasound images, and included four second-order statistical parameters (contrast, correlation, energy and homogeneity), each calculated at four different image directions (0°, 45°, 90°, 135°), yielding a total of 16 characteristics. Between high and low stenosis groups, 8 out of 16 characteristics were statistically different at the plaque shoulder at systole and 6 at diastole. No differences were observed between the two groups for any of the texture characteristics at the plaque nor at the adjacent wall. Differences in morphology along the arterial wall (wall - shoulder - plaque) were more pronounced in cases of high stenosis. The findings indicated that (a) the plaque shoulder is a particular area, requiring additional investigation so as to better understand the pathophysiology of atherosclerosis, (b) the phase of the cardiac cycle (systole or diastole) is important in texture analysis, and (c) the variability of morphology along the arterial wall, which is indicative of areas of tissue discontinuities, and therefore more vulnerable to rupture, can be characterized quantitatively with texture indices, toward an improved assessment of cardiovascular risk. It can be concluded that ultrasound-based texture indices may reveal novel markers for early detection and monitoring of subjects at high risk of cerebrovascular events, in the context of individualized, noninvasive and affordable diagnosis.

Multiscale geometric texture analysis of ultrasound images of carotid atherosclerosis

In this paper two wavelet extension methods, the ridgelet (DRT) and the fast discrete curvelet (FDCT) transforms, were used in an attempt to characterize carotid atherosclerotic plaque from B-mode ultrasound and 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 ranking the features in terms of their divergence values. The selected features were subsequently input to a classifier using support vector machines. Both transforms produced an 82.5% overall classification performance (75% and 85% for systole and 90% and 80% for diastole for DRT and FDCT, respectively). Those preliminary results in a somewhat limited sample population showed that, in terms of classification accuracy of ultrasound images of the carotid artery, ridgelet and curvelet transforms are equivalent. The faster and most sensitive FDCT algorithm might be a reasonable choice.

Multiscale approach for weighted least-squares optical flow for estimating arterial wall displacements

In this paper multiscale image decomposition was used to enhance the performance of weighted least-squares optical flow (WLSOF) in terms of estimating radial and longitudinal arterial wall displacements from B-mode ultrasound. For multiscale WLSOF (MWLSOF), ultrasound images were initially decomposed at one level using a 2D discrete wavelet transform, and WLSOF was applied on the resulting approximation images; the result was ‘translated’ to the original images through a coarse-to-fine transition process. WLSOF and MWLSOF were evaluated on synthetic image sequences of the common carotid artery. Multiscale image analysis increased the accuracy in displacement estimation, with MWLSOF yielding average displacement error reductions of 14% with respect to WLSOF. The methods were also effectively applied to real ultrasound image sequences of the carotid artery. It was concluded that MWLSOF can be efficiently used for estimating arterial wall displacements from B-mode ultrasound images.

Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based On DCE-MRI

Although Fourier and Wavelet Transform have been widely used for texture classification methods in medical images, the discrimination performance of FDCT has not been investigated so far in respect to breast cancer detection. Ιn this paper, three multi-resolution transforms, namely the Discrete Wavelet Transform (DWT), the Stationary Wavelet Transform (SWT) and the Fast Discrete Curvelet Transform (FDCT) were comparatively assessed with respect to their ability to discriminate between malignant and benign breast tumors in Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI). The mean and entropy of the detail sub-images for each decomposition scheme were used as texture features, which were subsequently fed as input into several classifiers. FDCT features fed to a Linear Discriminant Analysis (LDA) classifier produced the highest overall classification performance (93.18 % Accuracy).