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Meiburger K.M.,Polytechnic University of Turin | Molinari F.,Polytechnic University of Turin | Acharya U.R.,Ngee Ann Polytechnic | Saba L.,A.O.U. di Cagliari | And 6 more authors.
Physics in Medicine and Biology | Year: 2011

Evaluation of the carotid artery wall is essential for the assessment of a patient's cardiovascular risk or for the diagnosis of cardiovascular pathologies. This paper presents a new, completely user-independent algorithm called carotid artery intima layer regional segmentation (CAILRS, a class of AtheroEdge™ systems), which automatically segments the intima layer of the far wall of the carotid ultrasound artery based on mean shift classification applied to the far wall. Further, the system extracts the lumen-intima and media-adventitia borders in the far wall of the carotid artery. Our new system is characterized and validated by comparing CAILRS borders with the manual tracings carried out by experts. The new technique is also benchmarked with a semi-automatic technique based on a first-order absolute moment edge operator (FOAM) and compared to our previous edge-based automated methods such as CALEX (Molinari et al 2010 J. Ultrasound Med. 29 399-418, 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CULEX (Delsanto et al 2007 IEEE Trans. Instrum. Meas. 56 1265-74, Molinari et al 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CALSFOAM (Molinari et al Int. Angiol. (at press)), and CAUDLES-EF (Molinari et al J. Digit. Imaging (at press)). Our multi-institutional database consisted of 300 longitudinal B-mode carotid images. In comparison to semi-automated FOAM, CAILRS showed the IMT bias of -0.035 0.186 mm while FOAM showed -0.016 0.258 mm. Our IMT was slightly underestimated with respect to the ground truth IMT, but showed uniform behavior over the entire database. CAILRS outperformed all the four previous automated methods. The system's figure of merit was 95.6%, which was lower than that of the semi-automated method (98%), but higher than that of the other automated techniques. © 2011 Institute of Physics and Engineering in Medicine.

Aristokleous N.,Cyprus University of Technology | Seimenis I.,Democritus University of Thrace | Georgiou G.C.,University of Cyprus | Papaharilaou Y.,IACM FORTH | And 3 more authors.
IEEE Journal of Biomedical and Health Informatics | Year: 2014

This paper aims at evaluating the changes that head rotation poses on morphological and flow characteristics of the carotid bifurcation (CB) and on the distribution of parameters that are regarded as important in atherosclerosis development, such as relative particle residence time (RRT), normalized oscillatory shear index (nOSI), and helicity (HL). Using a subject-specific approach, six healthy volunteers were MR-scanned in two head postures: supine neutral and prone with rightward head rotation. Cross-sectional flow velocity distribution was obtained using phase-contrast MRI at the common carotid artery (CCA). Our results indicate that peak systolic flow rate is reduced at the prone position in most cases for both CCAs. Morphological MR images are used to segment and construct the CB models. Numerical simulations are performed and areas exposed to high helicity or unfavorable hemodynamics are calculated. Head rotation affects the instantaneous spatial extent of high helicity regions. Posture-related observed differences in the distribution of nOSI and RRT suggest that inlet flow waveform tends to moderate geometry-induced changes in the qualitative and quantitative distribution of atherosclerosis-susceptible wall regions. Overall, presented results indicate that an individualized approach is required to fully assess the postural role in atherosclerosis development and in complications arising in stenotic and stented vessels. © 2013 IEEE.

Molinari F.,Polytechnic University of Turin | Meiburger K.M.,Polytechnic University of Turin | Zeng G.,Mayo Medical School | Nicolaides A.,University of Cyprus | And 3 more authors.
Journal of Digital Imaging | Year: 2011

The evaluation of the carotid artery wall is essential for the diagnosis of cardiovascular pathologies or for the assessment of a patient's cardiovascular risk. This paper presents a completely user-independent algorithm, which automatically extracts the far double line (lumen- intima and media-adventitia) in the carotid artery using an Edge Flow technique based on directional probability maps using the attributes of intensity and texture. Specifically, the algorithm traces the boundaries between the lumen and intima layer (line one) and between the media and adventitia layer (line two). The Carotid Automated Ultrasound Double Line Extraction System based on Edge-Flow (CAUDLESEF) is characterized and validated by comparing the output of the algorithm with the manual tracing boundaries carried out by three experts. We also benchmark our new technique with the two other completely automatic techniques (CALEXia and CULEXsa) we previously published. Our multi-institutional database consisted of 300 longitudinal Bmode carotid images with normal and pathologic arteries. We compared our current new method with previous methods, and showed the mean and standard deviation for the three methods: CALEXia, CULEXsa, and CAUDLESEF as 0.134±0.088, 0.074±0.092, and 0.043±0.097 mm, respectively. Our IMT was slightly underestimated with respect to the ground truth IMT, but showed a uniform behavior over the entire database. Regarding the Figure of Merit (FoM), CALEXia and CULEXsa showed the values of 84.7% and 91.5%, respectively, while our new approach, CAUDLES-EF, performed the best at 94.8%, showing a good improvement compared to previous methods. © Society for Imaging Informatics in Medicine 2011.

Loizou C.P.,Intercollege | Murray V.,University of Lima | Murray V.,University of New Mexico | Pattichis M.S.,University of Lima | And 3 more authors.
International Journal of Biomedical Imaging | Year: 2014

The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). Typically, the IMT grows with age and this is used as a sign of increased risk of CVD. Beyond thickness, there is also clinical interest in identifying how the composition and texture of the intima-media complex (IMC) changed and how these textural changes grow into atherosclerotic plaques that can cause stroke. Clearly though texture analysis of ultrasound images can be greatly affected by speckle noise, our goal here is to develop effective despeckle noise methods that can recover image texture associated with increased rates of atherosclerosis disease. In this study, we perform a comparative evaluation of several despeckle filtering methods, on 100 ultrasound images of the CCA, based on the extracted multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) texture features and visual image quality assessment by two clinical experts. Texture features were extracted from the automatically segmented IMC for three different age groups. The despeckle filters hybrid median and the homogeneous mask area filter showed the best performance by improving the class separation between the three age groups and also yielded significantly improved image quality. © 2014 C. P. Loizou et al.

Acharya U.R.,Ngee Ann Polytechnic | Faust O.,Ngee Ann Polytechnic | Sree S.V.,Global Biomedical Technologies Inc. | Molinari F.,Polytechnic University of Turin | And 5 more authors.
IEEE Transactions on Instrumentation and Measurement | Year: 2012

Computer-aided diagnosis (CAD) of carotid atherosclerosis into symptomatic or asymptomatic is useful in the analysis of cardiac health. This paper describes a patented CAD system called Atheromatic™ for symptomatic versus asymptomatic plaque classification in carotid ultrasound images. The system involves two steps: 1) feature extraction using a combination of discrete wavelet transform and averaging algorithms and 2) classification using a support vector machine (SVM) classifier for automated decision making. The CAD system was evaluated using a database consisting of 150 asymptomatic and 196 symptomatic plaque regions which were labeled using the ground truth based on the presence or absence of symptoms. Threefold cross-validation protocol was adapted for developing and testing the classifiers. We observed that the SVM classifier with a polynomial kernel of order 2 was to achieve a classification accuracy of 83.7%. © 2011 IEEE.

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