An automated technique for carotid far wall classification using grayscale features and wall thickness variability

To test a computer‐aided diagnostic method for differentiating symptomatic from asymptomatic carotid B‐mode ultrasonographic images.

[1]  E. Song,et al.  Intima‐medial thickness homogeneity in the common carotid artery: Measurement method and preliminary clinical study , 2012, Journal of clinical ultrasound : JCU.

[2]  A. AbuRahma,et al.  Carotid Plaque Ultrasonic Heterogeneity and Severity of Stenosis , 2002, Stroke.

[3]  Marios S. Pattichis,et al.  A Review of Noninvasive Ultrasound Image Processing Methods in the Analysis of Carotid Plaque Morphology for the Assessment of Stroke Risk , 2010, IEEE Transactions on Information Technology in Biomedicine.

[4]  C. L. Nikias,et al.  Higher-order spectra analysis : a nonlinear signal processing framework , 1993 .

[5]  C. M. Lim,et al.  Analysis of epileptic EEG signals using higher order spectra , 2009, Journal of medical engineering & technology.

[6]  Chrysostomos L. Nikias,et al.  Higher-order spectral analysis , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[7]  Spyretta Golemati,et al.  Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks. , 2007, Ultrasound in medicine & biology.

[8]  Filippo Molinari,et al.  Carotid IMT Variability (IMTV) and Its Validation in Symptomatic versus Asymptomatic Italian Population: Can This Be a Useful Index for Studying Symptomaticity? , 2012, Echocardiography.

[9]  T. Elatrozy,et al.  The effect of B-mode ultrasonic image standardisation on the echodensity of symptomatic and asymptomatic carotid bifurcation plaques. , 1998, International angiology : a journal of the International Union of Angiology.

[10]  Filippo Molinari,et al.  Time-Frequency Characterization of Cerebral Hemodynamics of Migraine Sufferers as Assessed by NIRS Signals , 2010, EURASIP J. Adv. Signal Process..

[11]  J. Suri,et al.  What is the correct distance measurement metric when measuring carotid ultrasound intima-media thickness automatically? , 2012, International angiology : a journal of the International Union of Angiology.

[12]  M. Eliasziw,et al.  The causes and risk of stroke in patients with asymptomatic internal-carotid-artery stenosis. North American Symptomatic Carotid Endarterectomy Trial Collaborators. , 2000, The New England journal of medicine.

[13]  Matthias W. Lorenz,et al.  Prediction of Clinical Cardiovascular Events With Carotid Intima-Media Thickness: A Systematic Review and Meta-Analysis , 2007, Circulation.

[14]  Joao Sanches,et al.  Ultrasonographic characterization and identification of symptomatic carotid plaques , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[15]  E. Vicaut,et al.  Mannheim Carotid Intima-Media Thickness Consensus (2004–2006) , 2006, Cerebrovascular Diseases.

[16]  U. Rajendra Acharya,et al.  Completely Automated Multiresolution Edge Snapper—A New Technique for an Accurate Carotid Ultrasound IMT Measurement: Clinical Validation and Benchmarking on a Multi-Institutional Database , 2012, IEEE Transactions on Image Processing.

[17]  Constantinos S. Pattichis,et al.  Texture-based classification of atherosclerotic carotid plaques , 2003, IEEE Transactions on Medical Imaging.

[18]  Jasjit S. Suri,et al.  A state of the art review on intima-media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound , 2010, Comput. Methods Programs Biomed..

[19]  L. Gallo.  Cardiovascular Disease , 1995, GWUMC Department of Biochemistry Annual Spring Symposia.

[20]  U. Rajendra Acharya,et al.  Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization , 2013, Comput. Methods Programs Biomed..

[21]  Filippo Molinari,et al.  Fully Automated Dual‐Snake Formulation for Carotid Intima‐Media Thickness Measurement , 2012, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[22]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[23]  K. Williams,et al.  Atherosclerosis--an inflammatory disease. , 1999, The New England journal of medicine.

[24]  Lyndon Emsley,et al.  Two-dimensional statistical recoupling for the identification of perturbed metabolic networks from NMR spectroscopy. , 2010, Journal of proteome research.

[25]  Jasjit S. Suri,et al.  Greedy Technique and Its Validation for Fusion of Two Segmentation Paradigms Leads to an Accurate Intima–Media Thickness Measure in Plaque Carotid Arterial Ultrasound , 2010 .

[26]  U. Rajendra Acharya,et al.  Author's Personal Copy Biomedical Signal Processing and Control Automated Diagnosis of Epileptic Eeg Using Entropies , 2022 .

[27]  W. Brant,et al.  Hypoechoic plaque at US of the carotid artery: an independent risk factor for incident stroke in adults aged 65 years or older. Cardiovascular Health Study. , 1998, Radiology.

[28]  A. V.DavidSánchez,et al.  Advanced support vector machines and kernel methods , 2003, Neurocomputing.

[29]  U. Rajendra Acharya,et al.  Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment , 2013, Medical & Biological Engineering & Computing.

[30]  Andrew Nicolaides,et al.  Ultrasound imaging in the analysis of carotid plaque morphology for the assessment of stroke. , 2005, Studies in health technology and informatics.

[31]  U. Rajendra Acharya,et al.  Constrained snake vs. conventional snake for carotid ultrasound automated IMT measurements on multi-center data sets. , 2012, Ultrasonics.

[32]  R. Virmani,et al.  Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. , 2000, Arteriosclerosis, thrombosis, and vascular biology.