Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm
暂无分享,去创建一个
Petia Radeva | Jasjit S. Suri | Luca Saba | Narendra D. Londhe | Tadashi Araki | John R. Laird | Sumit K. Banchhor | J. Suri | L. Saba | P. Radeva | J. Laird | T. Araki | N. Londhe
[1] U. Rajendra Acharya,et al. Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound , 2012, Journal of Medical Systems.
[2] J. Suri,et al. Improved correlation between carotid and coronary atherosclerosis SYNTAX score using automated ultrasound carotid bulb plaque IMT measurement. , 2015, Ultrasound in medicine & biology.
[3] J. Suri,et al. Advanced algorithmic approaches to medical image segmentation: state-of-the-art application in cardiology, neurology, mammography and pathology , 2001 .
[4] Mark D. Huffman,et al. Executive Summary: Heart Disease and Stroke Statistics—2015 Update A Report From the American Heart Association , 2011, Circulation.
[5] J. Farmer,et al. Strategies for Multivessel Revascularization in Patients with Diabetes: The Freedom Trial , 2014, Current Atherosclerosis Reports.
[6] Renu Virmani,et al. Pathologic validation of a new method to quantify coronary calcific deposits in vivo using intravascular ultrasound , 2000 .
[7] V. Aboyans,et al. Carotid intima-media thickness as predictor of secondary events after coronary angioplasty. , 2003, International angiology : a journal of the International Union of Angiology.
[8] Filippo Molinari,et al. An automated technique for carotid far wall classification using grayscale features and wall thickness variability , 2015, Journal of clinical ultrasound : JCU.
[9] Marios S. Pattichis,et al. Classification of atherosclerotic carotid plaques using morphological analysis on ultrasound images , 2009, Applied Intelligence.
[10] U. Rajendra Acharya,et al. Inter- and intra-observer variability analysis of completely automated cIMT measurement software (AtheroEdge™) and its benchmarking against commercial ultrasound scanner and expert Readers , 2013, Comput. Biol. Medicine.
[11] Petia Radeva,et al. Well-balanced system for coronary calcium detection and volume measurement in a low resolution intravascular ultrasound videos , 2017, Comput. Biol. Medicine.
[12] Wei Liu,et al. Use of intravascular ultrasound vs. optical coherence tomography for mechanism and patterns of in-stent restenosis among bare metal stents and drug eluting stents. , 2016, Journal of thoracic disease.
[13] Milan Sonka,et al. Coronary plaque analysis by multimodality fusion. , 2005, Studies in health technology and informatics.
[14] Ajay Gupta,et al. Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: A web-based point-of-care tool for multicenter clinical trial , 2016, Comput. Biol. Medicine.
[15] Filippo Molinari,et al. Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement , 2010, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.
[16] Petia Radeva,et al. Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos , 2016, Journal of Medical Systems.
[17] D. Mozaffarian,et al. Executive summary: heart disease and stroke statistics--2012 update: a report from the American Heart Association. , 2012, Circulation.
[18] Petia Radeva,et al. Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach , 2016, Comput. Methods Programs Biomed..
[19] Filippo Molinari,et al. Association of automated carotid IMT measurement and HbA1c in Japanese patients with coronary artery disease. , 2013, Diabetes research and clinical practice.
[20] Jasjit S. Suri,et al. Ankle–Brachial Index and Its Link to Automated Carotid Ultrasound Measurement of Intima–Media Thickness Variability in 500 Japanese Coronary Artery Disease Patients , 2014, Current Atherosclerosis Reports.
[21] P. Serruys,et al. Ex vivo validation of 45 MHz intravascular ultrasound backscatter tissue characterization. , 2015, European heart journal cardiovascular Imaging.
[22] R. Ross,et al. Cell biology of atherosclerosis. , 1995, Annual review of physiology.
[23] 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.
[24] M. Matsumoto,et al. Carotid Intima-Media Thickness for Atherosclerosis. , 2016, Journal of atherosclerosis and thrombosis.
[25] Jasjit S. Suri,et al. Multi-Modality Atherosclerosis Imaging and Diagnosis , 2013 .
[26] Osamu Seguchi,et al. Atherosclerosis Found on Carotid Ultrasonography Is Associated With Atherosclerosis on Coronary Intravascular Ultrasonography , 2005, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.
[27] Paul Schoenhagen,et al. Understanding coronary artery disease: tomographic imaging with intravascular ultrasound , 2002, Heart.
[28] Jasjit S Suri,et al. Asymptomatic Carotid Disease—A New Tool for Assessing Neurological Risk , 2014, Echocardiography.
[29] Nobutaka Ikeda,et al. Impact of carotid artery ultrasound and ankle-brachial index on prediction of severity of SYNTAX score. , 2013, Circulation journal : official journal of the Japanese Circulation Society.
[30] Ayman El-Baz,et al. Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm , 2017, Comput. Biol. Medicine.
[31] Ajay Gupta,et al. Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm , 2017, Journal of Medical Systems.
[32] Robert M. Haralick,et al. Greedy Algorithm for Error Correction in Automatically Produced Boundaries from Low Contrast Ventriculograms , 2000, Pattern Analysis & Applications.
[33] Richard D. White,et al. Coronary imaging: angiography shows the stenosis, but IVUS, CT, and MRI show the plaque. , 2003, Cleveland Clinic journal of medicine.
[34] 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.
[35] 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.
[36] Constantinos S. Pattichis,et al. Texture-based classification of atherosclerotic carotid plaques , 2003, IEEE Transactions on Medical Imaging.
[37] J. Suri,et al. Volumetric Analysis of Carotid Plaque Components and Cerebral Microbleeds: A Correlative Study. , 2017, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.
[38] Nilanjan Dey,et al. A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound , 2015, Comput. Methods Programs Biomed..
[39] U Rajendra Acharya,et al. Automated carotid IMT measurement and its validation in low contrast ultrasound database of 885 patient Indian population epidemiological study: results of AtheroEdge™ Software. , 2012, International angiology : a journal of the International Union of Angiology.
[40] P. Libby,et al. Progress and challenges in translating the biology of atherosclerosis , 2011, Nature.
[41] Jasjit S. Suri,et al. A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens , 2016, Comput. Methods Programs Biomed..
[42] J. Suri,et al. Atherosclerotic risk stratification strategy for carotid arteries using texture-based features. , 2012, Ultrasound in medicine & biology.
[43] F. Lizzi. Ultrasound Imaging , 1991, Proceedings Technology Requirements for Biomedical Imaging.
[44] Nassir Navab,et al. A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images , 2012, IEEE Transactions on Information Technology in Biomedicine.
[45] R O Bonow,et al. Contrast magnetic resonance imaging in the assessment of myocardial viability in patients with stable coronary artery disease and left ventricular dysfunction. , 1998, Circulation.
[46] Filippo Molinari,et al. Shape‐Based Approach for Coronary Calcium Lesion Volume Measurement on Intravascular Ultrasound Imaging and Its Association With Carotid Intima‐Media Thickness , 2015, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.
[47] J. Borén,et al. Ira Tabas , Kevin Jon Williams and Jan Borén and Therapeutic Implications Subendothelial Lipoprotein Retention as the Initiating Process in Atherosclerosis : Update , 2007 .
[48] Tadashi Araki,et al. PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology , 2016, Comput. Methods Programs Biomed..
[49] U. Rajendra Acharya,et al. Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization , 2013, Comput. Methods Programs Biomed..
[50] M. Naghavi,et al. Vulnerable Atherosclerotic Plaque: A Multifocal Disease , 2003, Circulation.