{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:34:49Z","timestamp":1778168089995,"version":"3.51.4"},"reference-count":163,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T00:00:00Z","timestamp":1622592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T00:00:00Z","timestamp":1622592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"DOI":"10.1007\/s10278-021-00461-2","type":"journal-article","created":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T19:08:35Z","timestamp":1622660915000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular\/Stroke Risk Monitoring: Artificial Intelligence Framework"],"prefix":"10.1007","author":[{"given":"Mainak","family":"Biswas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Saba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toma\u017e","family":"Omerzu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amer M.","family":"Johri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Narendra N.","family":"Khanna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klaudija","family":"Viskovic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophie","family":"Mavrogeni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John R.","family":"Laird","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gyan","family":"Pareek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Miner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonella","family":"Balestrieri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Petros P","family":"Sfikakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Athanasios","family":"Protogerou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Durga Prasanna","family":"Misra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vikas","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George D","family":"Kitas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raghu","family":"Kolluri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditya","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vijay","family":"Viswanathan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zoltan","family":"Ruzsa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Nicolaides","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jasjit S.","family":"Suri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,2]]},"reference":[{"issue":"10","key":"461_CR1","doi-asserted-by":"publisher","first-page":"e56","DOI":"10.1161\/CIR.0000000000000659","volume":"139","author":"EJ Benjamin","year":"2019","unstructured":"Benjamin EJ, Muntner P, Bittencourt MS: Heart disease and stroke statistics-2019 update: a report from the American Heart Association. Circulation 2019, 139(10):e56-e528.","journal-title":"Circulation"},{"key":"461_CR2","unstructured":"Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, Chiuve SE, Cushman M, Delling FN, Deo R: Heart disease and stroke statistics\u20142018 update: a report from the American Heart Association. Circulation 2018."},{"issue":"2","key":"461_CR3","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1056\/NEJM199901143400207","volume":"340","author":"R Ross","year":"1999","unstructured":"Ross R: Atherosclerosis\u2014an inflammatory disease. New England journal of medicine 1999, 340(2):115-126.","journal-title":"New England journal of medicine"},{"key":"461_CR4","doi-asserted-by":"crossref","unstructured":"Suri JS, Kathuria C, Molinari F: Atherosclerosis disease management: Springer Science & Business Media; 2010.","DOI":"10.1007\/978-1-4419-7222-4"},{"key":"461_CR5","doi-asserted-by":"crossref","unstructured":"Saba L, Sanches JM, Pedro LM, Suri JS: Multi-modality atherosclerosis imaging and diagnosis: Springer; 2014.","DOI":"10.1007\/978-1-4614-7425-8"},{"key":"461_CR6","doi-asserted-by":"crossref","unstructured":"Libby P: The heart in COVID19: primary target or secondary bystander? JACC: Basic to Translational Science 2020.","DOI":"10.1016\/j.jacbts.2020.04.001"},{"issue":"4","key":"461_CR7","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1016\/j.cardiores.2004.05.001","volume":"63","author":"G Basta","year":"2004","unstructured":"Basta G, Schmidt AM, De Caterina R: Advanced glycation end products and vascular inflammation: implications for accelerated atherosclerosis in diabetes. Cardiovascular research 2004, 63(4):582-592.","journal-title":"Cardiovascular research"},{"issue":"1","key":"461_CR8","doi-asserted-by":"publisher","first-page":"121","DOI":"10.2337\/diacare.4.1.121","volume":"4","author":"JA Colwell","year":"1981","unstructured":"Colwell JA, Lopes-Virella M, Halushka PV: Pathogenesis of atherosclerosis in diabetes mellitus. Diabetes care 1981, 4(1):121-133.","journal-title":"Diabetes care"},{"issue":"2","key":"461_CR9","doi-asserted-by":"publisher","first-page":"e34","DOI":"10.1016\/j.atherosclerosis.2006.08.016","volume":"194","author":"T Saam","year":"2007","unstructured":"Saam T, Yuan C, Chu B, Takaya N, Underhill H, Cai J, Tran N, Polissar NL, Neradilek B, Jarvik GP: Predictors of carotid atherosclerotic plaque progression as measured by noninvasive magnetic resonance imaging. Atherosclerosis 2007, 194(2):e34-e42.","journal-title":"Atherosclerosis"},{"issue":"4","key":"461_CR10","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1159\/000229554","volume":"28","author":"H Hashimoto","year":"2009","unstructured":"Hashimoto H, Tagaya M, Niki H, Etani H: Computer-assisted analysis of heterogeneity on B-mode imaging predicts instability of asymptomatic carotid plaque. Cerebrovascular diseases 2009, 28(4):357-364.","journal-title":"Cerebrovascular diseases"},{"issue":"7","key":"461_CR11","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1056\/NEJM197608122950707","volume":"295","author":"R Ross","year":"1976","unstructured":"Ross R, Glomset JA: The pathogenesis of atherosclerosis. New England journal of medicine 1976, 295(7):369-377.","journal-title":"New England journal of medicine"},{"issue":"8","key":"461_CR12","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1056\/NEJM198602203140806","volume":"314","author":"R Ross","year":"1986","unstructured":"Ross R: The pathogenesis of atherosclerosis\u2014an update. New England Journal of Medicine 1986, 314(8):488-500.","journal-title":"New England Journal of Medicine"},{"issue":"5","key":"461_CR13","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1016\/S0741-5214(96)70237-9","volume":"23","author":"S Carr","year":"1996","unstructured":"Carr S, Farb A, Pearce WH, Virmani R, Yao JS: Atherosclerotic plaque rupture in symptomatic carotid artery stenosis. Journal of vascular surgery 1996, 23(5):755-766.","journal-title":"Journal of vascular surgery"},{"key":"461_CR14","unstructured":"Iadecola C: Revisiting atherosclerosis and dementia. Nature Neuroscience 2020:1\u20132."},{"issue":"12","key":"461_CR15","doi-asserted-by":"publisher","first-page":"4423","DOI":"10.1007\/s00330-016-4296-4","volume":"26","author":"P Lucatelli","year":"2016","unstructured":"Lucatelli P, Raz E, Saba L, Argiolas GM, Montisci R, Wintermark M, King KS, Molinari F, Ikeda N, Siotto P: Relationship between leukoaraiosis, carotid intima-media thickness and intima-media thickness variability: Preliminary results. European radiology 2016, 26(12):4423-4431.","journal-title":"European radiology"},{"issue":"8","key":"461_CR16","doi-asserted-by":"publisher","first-page":"1824","DOI":"10.1016\/j.jstrokecerebrovasdis.2017.04.016","volume":"26","author":"L Saba","year":"2017","unstructured":"Saba L, Sanfilippo R, Balestrieri A, Zaccagna F, Argiolas GM, Suri JS, Montisci R: Relationship between Carotid Computed Tomography Dual-Energy and Brain Leukoaraiosis. Journal of Stroke and Cerebrovascular Diseases 2017, 26(8):1824-1830.","journal-title":"Journal of Stroke and Cerebrovascular Diseases"},{"key":"461_CR17","doi-asserted-by":"crossref","unstructured":"Wingo AP, Fan W, Duong DM, Gerasimov ES, Dammer EB, Liu Y, Harerimana NV, White B, Thambisetty M, Troncoso JC: Shared proteomic effects of cerebral atherosclerosis and Alzheimer\u2019s disease on the human brain. Nature Neuroscience 2020:1\u20135.","DOI":"10.1038\/s41593-020-0662-2"},{"key":"461_CR18","doi-asserted-by":"crossref","unstructured":"Viswanathan V, Jamthikar AD, Gupta D, Puvvula A, Khanna NN, Saba L, Viskovic K, Mavrogeni S, Turk M, Laird JR: Integration of eGFR biomarker in image-based CV\/Stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease. International Angiology: a Journal of the International Union of Angiology 2020.","DOI":"10.23736\/S0392-9590.20.04338-2"},{"key":"461_CR19","doi-asserted-by":"crossref","unstructured":"Puvvula A, Jamthikar AD, Gupta D, Khanna NN, Porcu M, Saba L, Viskovic K, Ajuluchukwu JN, Gupta A, Mavrogeni S: Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease. Angiology 2020:0003319720910660.","DOI":"10.1177\/0003319720910660"},{"issue":"2","key":"461_CR20","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s11883-019-0766-x","volume":"21","author":"NN Khanna","year":"2019","unstructured":"Khanna NN, Jamthikar AD, Gupta D, Piga M, Saba L, Carcassi C, Giannopoulos AA, Nicolaides A, Laird JR, Suri HS: Rheumatoid arthritis: atherosclerosis imaging and cardiovascular risk assessment using machine and deep learning\u2013based tissue characterization. Current atherosclerosis reports 2019, 21(2):7.","journal-title":"Current atherosclerosis reports"},{"issue":"7","key":"461_CR21","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s11883-018-0736-8","volume":"20","author":"A Boi","year":"2018","unstructured":"Boi A, Jamthikar AD, Saba L, Gupta D, Sharma A, Loi B, Laird JR, Khanna NN, Suri JS: A survey on coronary atherosclerotic plaque tissue characterization in intravascular optical coherence tomography. Current atherosclerosis reports 2018, 20(7):33.","journal-title":"Current atherosclerosis reports"},{"key":"461_CR22","unstructured":"Liu K, Suri JS: Automatic vessel indentification for angiographic screening. In.: Google Patents; 2005."},{"issue":"5","key":"461_CR23","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1109\/TUFFC.2010.1522","volume":"57","author":"F Molinari","year":"2010","unstructured":"Molinari F, Zeng G, Suri JS: Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement. IEEE transactions on ultrasonics, ferroelectrics, and frequency control 2010, 57(5):1112-1124.","journal-title":"IEEE transactions on ultrasonics, ferroelectrics, and frequency control"},{"issue":"3","key":"461_CR24","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1109\/TIP.2011.2169270","volume":"21","author":"F Molinari","year":"2011","unstructured":"Molinari F, Pattichis CS, Zeng G, Saba L, Acharya UR, Sanfilippo R, Nicolaides A, Suri JS: Completely automated multiresolution edge snapper\u2014a new technique for an accurate carotid ultrasound IMT measurement: clinical validation and benchmarking on a multi-institutional database. IEEE Transactions on image processing 2011, 21(3):1211-1222.","journal-title":"IEEE Transactions on image processing"},{"issue":"7","key":"461_CR25","doi-asserted-by":"publisher","first-page":"1818","DOI":"10.1161\/STROKEAHA.111.646596","volume":"43","author":"M Herder","year":"2012","unstructured":"Herder M, Johnsen SH, Arntzen KA, Mathiesen EB: Risk factors for progression of carotid intima-media thickness and total plaque area: a 13-year follow-up study: the Troms\u00f8 Study. Stroke 2012, 43(7):1818-1823.","journal-title":"Stroke"},{"issue":"2","key":"461_CR26","first-page":"775","volume":"90","author":"PR Moreno","year":"1994","unstructured":"Moreno PR, Falk E, Palacios IF, Newell JB, Fuster V, Fallon JT: Macrophage infiltration in acute coronary syndromes. Implications for plaque rupture. Circulation 1994, 90(2):775-778.","journal-title":"Implications for plaque rupture. Circulation"},{"key":"461_CR27","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.compbiomed.2018.08.008","volume":"101","author":"E Cuadrado-Godia","year":"2018","unstructured":"Cuadrado-Godia E, Maniruzzaman M, Araki T, Puvvula A, Rahman MJ, Saba L, Suri HS, Gupta A, Banchhor SK, Teji JS: Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in diabetes cohort. Computers in biology and medicine 2018, 101:128-145.","journal-title":"Computers in biology and medicine"},{"key":"461_CR28","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.diabres.2018.07.028","volume":"143","author":"V Kotsis","year":"2018","unstructured":"Kotsis V, Jamthikar AD, Araki T, Gupta D, Laird JR, Giannopoulos AA, Saba L, Suri HS, Mavrogeni S, Kitas GD: Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients. Diabetes research and clinical practice 2018, 143:322-331.","journal-title":"Diabetes research and clinical practice"},{"key":"461_CR29","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.compbiomed.2017.10.022","volume":"91","author":"L Saba","year":"2017","unstructured":"Saba L, Banchhor SK, Londhe ND, Araki T, Laird JR, Gupta A, Nicolaides A, Suri JS: Web-based accurate measurements of carotid lumen diameter and stenosis severity: an ultrasound-based clinical tool for stroke risk assessment during multicenter clinical trials. Computers in Biology and Medicine 2017, 91:306-317.","journal-title":"Computers in Biology and Medicine"},{"issue":"5","key":"461_CR30","first-page":"445","volume":"36","author":"H Baradaran","year":"2017","unstructured":"Baradaran H, Ng CR, Gupta A, Noor NM, Al-Dasuqi KW, Mtui EE, Rijal OM, Giannopoulos A, Nicolaides A, Laird JR: Extracranial internal carotid artery calcium volume measurement using computer tomography. International angiology: a journal of the International Union of Angiology 2017, 36(5):445-461.","journal-title":"International angiology: a journal of the International Union of Angiology"},{"issue":"6","key":"461_CR31","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/s10916-016-0504-7","volume":"40","author":"L Saba","year":"2016","unstructured":"Saba L, Than JC, Noor NM, Rijal OM, Kassim RM, Yunus A, Ng CR, Suri JS: Inter-observer variability analysis of automatic lung delineation in normal and disease patients. Journal of medical systems 2016, 40(6):142.","journal-title":"Journal of medical systems"},{"issue":"3","key":"461_CR32","first-page":"290","volume":"34","author":"L Saba","year":"2015","unstructured":"Saba L, Bhavsar A, Gupta A, Mtui E, Giambrone A, Baradaran H, Lavra F, Laird J, Nicolaides A, Suri J: Automated calcium burden measurement in internal carotid artery plaque with CT: a hierarchical adaptive approach. International angiology: a journal of the International Union of Angiology 2015, 34(3):290-305.","journal-title":"International angiology: a journal of the International Union of Angiology"},{"issue":"1","key":"461_CR33","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.amjcard.2004.03.023","volume":"94","author":"DM Lloyd-Jones","year":"2004","unstructured":"Lloyd-Jones DM, Wilson PW, Larson MG, Beiser A, Leip EP, D'Agostino RB, Levy D: Framingham risk score and prediction of lifetime risk for coronary heart disease. The American journal of cardiology 2004, 94(1):20-24.","journal-title":"The American journal of cardiology"},{"issue":"5","key":"461_CR34","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/j.cjca.2015.02.001","volume":"31","author":"D Preiss","year":"2015","unstructured":"Preiss D, Kristensen SL: The new pooled cohort equations risk calculator. Canadian journal of Cardiology 2015, 31(5):613-619.","journal-title":"Canadian journal of Cardiology"},{"issue":"6","key":"461_CR35","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1001\/jama.297.6.611","volume":"297","author":"PM Ridker","year":"2007","unstructured":"Ridker PM, Buring JE, Rifai N, Cook NR: Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. Jama 2007, 297(6):611-619.","journal-title":"Jama"},{"issue":"6","key":"461_CR36","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1042\/CS20000335","volume":"101","author":"RJ Stevens","year":"2001","unstructured":"Stevens RJ, Kothari V, Adler AI, Stratton IM, Holman RR, Group UKPDS: The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56). Clinical science 2001, 101(6):671-679.","journal-title":"Clinical science"},{"issue":"7","key":"461_CR37","doi-asserted-by":"publisher","first-page":"1776","DOI":"10.1161\/01.STR.0000020091.07144.C7","volume":"33","author":"V Kothari","year":"2002","unstructured":"Kothari V, Stevens RJ, Adler AI, Stratton IM, Manley SE, Neil HA, Holman RR: UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine. Stroke 2002, 33(7):1776-1781.","journal-title":"Stroke"},{"issue":"1","key":"461_CR38","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1136\/heartjnl-2013-304474","volume":"100","author":"Tillin T, Hughes AD, Whincup P, Mayet J, Sattar N, McKeigue PM, Chaturvedi N, Group SS","year":"2014","unstructured":"Tillin T, Hughes AD, Whincup P, Mayet J, Sattar N, McKeigue PM, Chaturvedi N, Group SS: Ethnicity and prediction of cardiovascular disease: performance of QRISK2 and Framingham scores in a UK tri-ethnic prospective cohort study (SABRE\u2014Southall And Brent REvisited). Heart 2014, 100(1):60-67.","journal-title":"Heart"},{"key":"461_CR39","doi-asserted-by":"crossref","unstructured":"Board JBS: Joint British Societies\u2019 consensus recommendations for the prevention of cardiovascular disease (JBS3). Heart 2014, 100(Suppl 2):ii1-ii67.","DOI":"10.1136\/heartjnl-2014-305693"},{"key":"461_CR40","doi-asserted-by":"crossref","unstructured":"Seabra J, Sanches J: Ultrasound Imaging: Advances and Applications. In.: New York: Springer; 2012.","DOI":"10.1007\/978-1-4614-1180-2"},{"key":"461_CR41","doi-asserted-by":"crossref","unstructured":"Suri JS, Laxminarayan S: Angiography and plaque imaging: advanced segmentation techniques: CRC press; 2003.","DOI":"10.1201\/9780203490907"},{"key":"461_CR42","doi-asserted-by":"crossref","unstructured":"Molinari F, M. Meiburger K, Zeng G, Acharya UR, Liboni W, Nicolaides A, Suri JS: Carotid artery recognition system: a comparison of three automated paradigms for ultrasound images. Medical physics 2012, 39(1):378\u2013391.","DOI":"10.1118\/1.3670373"},{"key":"461_CR43","doi-asserted-by":"crossref","unstructured":"Molinari F, Meiburger KM, Acharya UR, Zeng G, Rodrigues PS, Saba L, Nicolaides A, Suri JS: CARES 3.0: a two stage system combining feature-based recognition and edge-based segmentation for CIMT measurement on a multi-institutional ultrasound database of 300 images. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 2011. IEEE: 5149\u20135152.","DOI":"10.1109\/IEMBS.2011.6091275"},{"issue":"11","key":"461_CR44","doi-asserted-by":"publisher","first-page":"2195","DOI":"10.1161\/01.STR.28.11.2195","volume":"28","author":"I Wendelhag","year":"1997","unstructured":"Wendelhag I, Liang Q, Gustavsson T, Wikstrand J: A new automated computerized analyzing system simplifies readings and reduces the variability in ultrasound measurement of intima-media thickness. Stroke 1997, 28(11):2195-2200.","journal-title":"Stroke"},{"issue":"1","key":"461_CR45","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/S0169-2607(00)00149-8","volume":"67","author":"D-c Cheng","year":"2002","unstructured":"Cheng D-c, Schmidt-Trucks\u00e4ss A, Cheng K-s, Burkhardt H: Using snakes to detect the intimal and adventitial layers of the common carotid artery wall in sonographic images. Computer methods and programs in biomedicine 2002, 67(1):27-37.","journal-title":"Computer methods and programs in biomedicine"},{"issue":"8","key":"461_CR46","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1007\/s11517-016-1601-y","volume":"55","author":"PK Kumar","year":"2017","unstructured":"Kumar PK, Araki T, Rajan J, Saba L, Lavra F, Ikeda N, Sharma AM, Shafique S, Nicolaides A, Laird JR: Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach. Medical & biological engineering & computing 2017, 55(8):1415-1434.","journal-title":"Medical & biological engineering & computing"},{"key":"461_CR47","doi-asserted-by":"crossref","unstructured":"Ikeda N, Gupta A, Dey N, Bose S, Shafique S, Arak T, Godia EC, Saba L, Laird JR, Nicolaides AJUim et al: Improved correlation between carotid and coronary atherosclerosis SYNTAX score using automated ultrasound carotid bulb plaque IMT measurement. 2015, 41(5):1247\u20131262.","DOI":"10.1016\/j.ultrasmedbio.2014.12.024"},{"key":"461_CR48","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.cmpb.2017.01.009","volume":"141","author":"N Ikeda","year":"2017","unstructured":"Ikeda N, Dey N, Sharma A, Gupta A, Bose S, Acharjee S, Shafique S, Cuadrado-Godia E, Araki T, Saba L: Automated segmental-IMT measurement in thin\/thick plaque with bulb presence in carotid ultrasound from multiple scanners: Stroke risk assessment. Computer methods and programs in biomedicine 2017, 141:73-81.","journal-title":"Computer methods and programs in biomedicine"},{"key":"461_CR49","unstructured":"Han J, Pei J, Kamber M: Data mining: concepts and techniques: Elsevier; 2011."},{"issue":"7553","key":"461_CR50","first-page":"436","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G: Deep learning. nature 2015, 521(7553):436-444.","journal-title":"Hinton G: Deep learning. nature"},{"issue":"4","key":"461_CR51","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1215\/S0012-7094-50-01743-1","volume":"17","author":"HG Eggleston","year":"1950","unstructured":"Eggleston HG: A property of Hausdorff measure. Duke Mathematical Journal 1950, 17(4):491-498.","journal-title":"Duke Mathematical Journal"},{"issue":"5","key":"461_CR52","first-page":"483","volume":"31","author":"L Saba","year":"2012","unstructured":"Saba L, Molinari F, Meiburger K, Piga M, Zeng G, Rajendra UA, Nicolaides A, Suri J: What is the correct distance measurement metric when measuring carotid ultrasound intima-media thickness automatically? International angiology: a journal of the International Union of Angiology 2012, 31(5):483-489.","journal-title":"International angiology: a journal of the International Union of Angiology"},{"issue":"1\u201312","key":"461_CR53","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.permed.2012.01.006","volume":"1","author":"S Bartels","year":"2012","unstructured":"Bartels S, Franco AR, Rundek T: Carotid intima-media thickness (cIMT) and plaque from risk assessment and clinical use to genetic discoveries. Perspectives in Medicine 2012, 1(1-12):139-145.","journal-title":"Perspectives in Medicine"},{"key":"461_CR54","unstructured":"Gustavsson T, Liang Q, Wendelhag I, Wikstrand J: A dynamic programming procedure for automated ultrasonic measurement of the carotid artery. In: Computers in Cardiology 1994: 1994. IEEE: 297\u2013300."},{"key":"461_CR55","doi-asserted-by":"crossref","unstructured":"Ballard DH: Generalizing the Hough transform to detect arbitrary shapes. In: Readings in computer vision. Elsevier; 1987: 714\u2013725.","DOI":"10.1016\/B978-0-08-051581-6.50069-6"},{"issue":"8","key":"461_CR56","doi-asserted-by":"publisher","first-page":"2202","DOI":"10.1109\/TBME.2011.2127476","volume":"58","author":"F Destrempes","year":"2011","unstructured":"Destrempes F, Meunier J, Giroux M-F, Soulez G, Cloutier G: Segmentation of plaques in sequences of ultrasonic B-mode images of carotid arteries based on motion estimation and a Bayesian model. IEEE Transactions on Biomedical Engineering 2011, 58(8):2202-2211.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"461_CR57","doi-asserted-by":"crossref","unstructured":"Giraldi G, Rodrigues P, Suri J, Singh S: Dual active contour models for medical image segmentation. Image Segmentation 2011:129.","DOI":"10.5772\/15044"},{"key":"461_CR58","unstructured":"Suri JS, Haralick RM, Sheehan F, Jamin V: Effect of edge detection, pixel classification, and classification-edge fusion over LV calibration: A two stage automatic system. In: SCIA'97: 10th Scandinavian conference on image analysis (Lappeeranta, June 9\u201311, 1997): 1997. 197\u2013204."},{"issue":"12","key":"461_CR59","doi-asserted-by":"publisher","first-page":"1618","DOI":"10.1109\/TIP.2003.819311","volume":"12","author":"PR Hill","year":"2003","unstructured":"Hill PR, Canagarajah CN, Bull DR: Image segmentation using a texture gradient based watershed transform. IEEE Transactions on Image Processing 2003, 12(12):1618-1633.","journal-title":"IEEE Transactions on Image Processing"},{"key":"461_CR60","unstructured":"El-Baz A, Suri JS: Lung imaging and computer aided diagnosis: CRC Press; 2011."},{"key":"461_CR61","doi-asserted-by":"crossref","unstructured":"Radeva P, Suri JS: Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 2; Plaque characterization. vii2 2019.","DOI":"10.1088\/2053-2563\/ab0820"},{"key":"461_CR62","doi-asserted-by":"crossref","unstructured":"Molinari F, Zeng G, Suri JS: Carotid wall segmentation and IMT measurement in longitudinal ultrasound images using morphological approach. In: International symposium on biomedical imaging, Rotterdam: 2010.","DOI":"10.1109\/ISBI.2010.5490340"},{"issue":"3","key":"461_CR63","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1016\/j.cmpb.2012.05.008","volume":"108","author":"F Molinari","year":"2012","unstructured":"Molinari F, Meiburger KM, Saba L, Acharya UR, Ledda G, Zeng G, Ho SYS, Ahuja AT, Ho SC, Nicolaides A: Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: our review and experience using four fully automated and one semi-automated methods. Computer methods and programs in biomedicine 2012, 108(3):946-960.","journal-title":"Computer methods and programs in biomedicine"},{"issue":"12","key":"461_CR64","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s11883-016-0635-9","volume":"18","author":"AK Patel","year":"2016","unstructured":"Patel AK, Suri HS, Singh J, Kumar D, Shafique S, Nicolaides A, Jain SK, Saba L, Gupta A, Laird JR: A review on atherosclerotic biology, wall stiffness, physics of elasticity, and its ultrasound-based measurement. Current atherosclerosis reports 2016, 18(12):83.","journal-title":"Current atherosclerosis reports"},{"key":"461_CR65","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.cmpb.2018.05.015","volume":"163","author":"PK Kumar","year":"2018","unstructured":"Kumar PK, Araki T, Rajan J, Laird JR, Nicolaides A, Suri JS: State-of-the-art review on automated lumen and adventitial border delineation and its measurements in carotid ultrasound. Computer methods and programs in biomedicine 2018, 163:155-168.","journal-title":"Computer methods and programs in biomedicine"},{"key":"461_CR66","doi-asserted-by":"crossref","unstructured":"Molinari F, Meiburger KM, Suri J: Automated high-performance cIMT measurement techniques using patented AtheroEdge\u2122: A screening and home monitoring system. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 2011. IEEE: 6651\u20136654.","DOI":"10.1109\/IEMBS.2011.6091640"},{"issue":"12","key":"461_CR67","doi-asserted-by":"publisher","first-page":"1918","DOI":"10.1016\/j.ultrasmedbio.2007.05.021","volume":"33","author":"S Golemati","year":"2007","unstructured":"Golemati S, Stoitsis J, Sifakis EG, Balkizas T, Nikita KS: Using the Hough transform to segment ultrasound images of longitudinal and transverse sections of the carotid artery. Ultrasound in medicine & biology 2007, 33(12):1918-1932.","journal-title":"Ultrasound in medicine & biology"},{"key":"461_CR68","doi-asserted-by":"crossref","unstructured":"Stoitsis J, Golemati S, Kendros S, Nikita K: Automated detection of the carotid artery wall in B-mode ultrasound images using active contours initialized by the Hough transform. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 2008. IEEE: 3146\u20133149.","DOI":"10.1109\/IEMBS.2008.4649871"},{"key":"461_CR69","doi-asserted-by":"crossref","unstructured":"Petroudi S, Loizou CP, Pattichis CS: Atherosclerotic carotid wall segmentation in ultrasound images using Markov random fields. In: Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine: 2010. IEEE: 1\u20135.","DOI":"10.1109\/ITAB.2010.5687685"},{"issue":"4","key":"461_CR70","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1109\/TITB.2002.804139","volume":"6","author":"JS Suri","year":"2002","unstructured":"Suri JS, Liu K, Reden L, Laxminarayan S: A review on MR vascular image processing algorithms: acquisition and prefiltering: part I. IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 2002, 6(4):324.","journal-title":"IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society"},{"key":"461_CR71","doi-asserted-by":"crossref","unstructured":"Delsanto S, Molinari F, Giustetto P, Liboni W, Badalamenti S: CULEX-completely user-independent layers extraction: ultrasonic carotid artery images segmentation. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference: 2006. IEEE: 6468\u20136471.","DOI":"10.1109\/IEMBS.2005.1615980"},{"issue":"4","key":"461_CR72","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1109\/TIM.2011.2174901","volume":"61","author":"F Molinari","year":"2012","unstructured":"Molinari F, Krishnamurthi G, Acharya UR, Sree SV, Zeng G, Saba L, Nicolaides A, Suri JS: Hypothesis validation of far-wall brightness in carotid-artery ultrasound for feature-based IMT measurement using a combination of level-set segmentation and registration. IEEE Transactions on Instrumentation and measurement 2012, 61(4):1054-1063.","journal-title":"IEEE Transactions on Instrumentation and measurement"},{"issue":"3","key":"461_CR73","first-page":"227","volume":"30","author":"F Molinari","year":"2011","unstructured":"Molinari F, Liboni W, Pantziaris M, Suri J: CALSFOAM-completed automated local statistics based first order absolute moment\" for carotid wall recognition, segmentation and IMT measurement: validation and benchmarking on a 300 patient database. International angiology: a journal of the International Union of Angiology 2011, 30(3):227-241.","journal-title":"International angiology: a journal of the International Union of Angiology"},{"issue":"4","key":"461_CR74","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/BF00133570","volume":"1","author":"M Kass","year":"1988","unstructured":"Kass M, Witkin A, Terzopoulos D: Snakes: Active contour models. International journal of computer vision 1988, 1(4):321-331.","journal-title":"International journal of computer vision"},{"issue":"3","key":"461_CR75","doi-asserted-by":"publisher","first-page":"399","DOI":"10.7863\/jum.2010.29.3.399","volume":"29","author":"F Molinari","year":"2010","unstructured":"Molinari F, Zeng G, Suri JS: An integrated approach to computer\u2010based automated tracing and its validation for 200 common carotid arterial wall ultrasound images: A new technique. Journal of Ultrasound in Medicine 2010, 29(3):399-418.","journal-title":"Journal of Ultrasound in Medicine"},{"issue":"1","key":"461_CR76","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/4233.992158","volume":"6","author":"JS Suri","year":"2002","unstructured":"Suri JS, Liu K, Singh S, Laxminarayan SN, Zeng X, Reden L: Shape recovery algorithms using level sets in 2-D\/3-D medical imagery: a state-of-the-art review. IEEE Transactions on information technology in biomedicine 2002, 6(1):8-28.","journal-title":"IEEE Transactions on information technology in biomedicine"},{"issue":"6","key":"461_CR77","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1109\/19.982968","volume":"50","author":"C Liguori","year":"2001","unstructured":"Liguori C, Paolillo A, Pietrosanto A: An automatic measurement system for the evaluation of carotid intima-media thickness. IEEE Transactions on instrumentation and measurement 2001, 50(6):1684-1691.","journal-title":"IEEE Transactions on instrumentation and measurement"},{"key":"461_CR78","doi-asserted-by":"crossref","unstructured":"Molinari F, Zeng G, Suri JS: Inter-greedy technique for fusion of different segmentation strategies leading to high-performance carotid IMT measurement in ultrasound images. In: Atherosclerosis Disease Management. Springer; 2011: 253\u2013279.","DOI":"10.1007\/978-1-4419-7222-4_10"},{"key":"461_CR79","doi-asserted-by":"crossref","unstructured":"Khanna NN, Jamthikar AD, Gupta D, Araki T, Piga M, Saba L, Carcassi C, Nicolaides A, Laird JR, Suri HS: Effect of carotid image-based phenotypes on cardiovascular risk calculator: AECRS1. 0. Medical & biological engineering & computing 2019, 57(7):1553\u20131566.","DOI":"10.1007\/s11517-019-01975-2"},{"issue":"11","key":"461_CR80","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1161\/CIRCULATIONAHA.119.041980","volume":"140","author":"MP Than","year":"2019","unstructured":"Than MP, Pickering JW, Sandoval Y, Shah AS, Tsanas A, Apple FS, Blankenberg S, Cullen L, Mueller C, Neumann JT: Machine learning to predict the likelihood of acute myocardial infarction. Circulation 2019, 140(11):899-909.","journal-title":"Circulation"},{"issue":"9","key":"461_CR81","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1161\/CIRCRESAHA.117.311312","volume":"121","author":"B Ambale-Venkatesh","year":"2017","unstructured":"Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, Gomes AS, Folsom AR, Shea S, Guallar E: Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circulation research 2017, 121(9):1092-1101.","journal-title":"Circulation research"},{"key":"461_CR82","doi-asserted-by":"publisher","first-page":"392","DOI":"10.2741\/4725","volume":"24","author":"M Biswas","year":"2019","unstructured":"Biswas M, Kuppili V, Saba L, Edla DR, Suri HS, Cuadrado-Godia E, Laird J, Marinhoe R, Sanches J, Nicolaides A: State-of-the-art review on deep learning in medical imaging. Front Biosci (Landmark Ed) 2019, 24:392-426.","journal-title":"Front Biosci (Landmark Ed)"},{"key":"461_CR83","unstructured":"Bishop CM: Pattern recognition and machine learning: springer; 2006."},{"key":"461_CR84","unstructured":"Krizhevsky A, Sutskever I, Hinton GE: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems: 2012. 1097\u20131105."},{"key":"461_CR85","doi-asserted-by":"crossref","unstructured":"Naik V, Gamad R, Bansod P: Carotid artery segmentation in ultrasound images and measurement of intima-media thickness. BioMed research international 2013, 2013.","DOI":"10.1155\/2013\/801962"},{"issue":"12","key":"461_CR86","doi-asserted-by":"publisher","first-page":"2916","DOI":"10.1161\/01.STR.0000042207.16156.B9","volume":"33","author":"JD Spence","year":"2002","unstructured":"Spence JD, Eliasziw M, DiCicco M, Hackam DG, Galil R, Lohmann T: Carotid plaque area: a tool for targeting and evaluating vascular preventive therapy. Stroke 2002, 33(12):2916-2922.","journal-title":"Stroke"},{"issue":"4","key":"461_CR87","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/S0002-9149(01)02327-X","volume":"89","author":"JD Spence","year":"2002","unstructured":"Spence JD: Ultrasound measurement of carotid plaque as a surrogate outcome for coronary artery disease. The American journal of cardiology 2002, 89(4):10-15.","journal-title":"The American journal of cardiology"},{"issue":"4","key":"461_CR88","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1161\/STROKEAHA.110.589754","volume":"42","author":"EB Mathiesen","year":"2011","unstructured":"Mathiesen EB, Johnsen SH, Wilsgaard T, B\u00f8naa KH, L\u00f8chen M-L, Nj\u00f8lstad I: Carotid plaque area and intima-media thickness in prediction of first-ever ischemic stroke: a 10-year follow-up of 6584 men and women: the Troms\u00f8 Study. Stroke 2011, 42(4):972-978.","journal-title":"Stroke"},{"key":"461_CR89","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.compbiomed.2019.01.002","volume":"105","author":"NN Khanna","year":"2019","unstructured":"Khanna NN, Jamthikar AD, Gupta D, Nicolaides A, Araki T, Saba L, Cuadrado-Godia E, Sharma A, Omerzu T, Suri HS: Performance evaluation of 10-year ultrasound image-based stroke\/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: a diabetic study. Computers in biology and medicine 2019, 105:125-143.","journal-title":"Computers in biology and medicine"},{"issue":"6","key":"461_CR90","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1016\/j.bspc.2013.08.008","volume":"8","author":"RJ Martis","year":"2013","unstructured":"Martis RJ, Acharya UR, Prasad H, Chua CK, Lim CM, Suri JS: Application of higher order statistics for atrial arrhythmia classification. Biomedical signal processing and control 2013, 8(6):888-900.","journal-title":"Biomedical signal processing and control"},{"key":"461_CR91","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.cmpb.2017.09.004","volume":"152","author":"M Maniruzzaman","year":"2017","unstructured":"Maniruzzaman M, Kumar N, Menhazul Abedin M, Shaykhul Islam M, Suri HS, El-Baz AS, Suri JS: Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm. Comput Methods Programs Biomed 2017, 152:23-34.","journal-title":"Comput Methods Programs Biomed"},{"issue":"2","key":"461_CR92","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s11517-013-1128-4","volume":"52","author":"R-M Mench\u00f3n-Lara","year":"2014","unstructured":"Mench\u00f3n-Lara R-M, Bastida-Jumilla M-C, Morales-S\u00e1nchez J, Sancho-G\u00f3mez J-L: Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks. Medical & biological engineering & computing 2014, 52(2):169-181.","journal-title":"Medical & biological engineering & computing"},{"key":"461_CR93","doi-asserted-by":"crossref","unstructured":"Mench\u00f3n-Lara R-M, Sancho-G\u00f3mez J-L: Ultrasound image processing based on machine learning for the fully automatic evaluation of the Carotid Intima-Media Thickness. In: 2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI): 2014. IEEE: 1\u20134.","DOI":"10.1109\/CBMI.2014.6849839"},{"issue":"1","key":"461_CR94","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/0165-1684(94)90060-4","volume":"38","author":"F Meyer","year":"1994","unstructured":"Meyer F: Topographic distance and watershed lines. Signal processing 1994, 38(1):113-125.","journal-title":"Signal processing"},{"key":"461_CR95","doi-asserted-by":"crossref","unstructured":"Elnakib A, Gimel\u2019farb G, Suri JS, El-Baz A: Medical image segmentation: a brief survey. In: Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies. Springer; 2011: 1\u201339.","DOI":"10.1007\/978-1-4419-8204-9_1"},{"issue":"3","key":"461_CR96","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s100440070008","volume":"3","author":"JS Suri","year":"2000","unstructured":"Suri JS: Computer vision, pattern recognition and image processing in left ventricle segmentation: The last 50 years. Pattern Analysis & Applications 2000, 3(3):209-242.","journal-title":"Pattern Analysis & Applications"},{"issue":"2","key":"461_CR97","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/BF02811896","volume":"19","author":"B Yegnanarayana","year":"1994","unstructured":"Yegnanarayana B: Artificial neural networks for pattern recognition. Sadhana 1994, 19(2):189-238.","journal-title":"Sadhana"},{"issue":"4\u20135","key":"461_CR98","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/S0893-6080(01)00027-2","volume":"14","author":"F Schwenker","year":"2001","unstructured":"Schwenker F, Kestler HA, Palm G: Three learning phases for radial-basis-function networks. Neural networks 2001, 14(4-5):439-458.","journal-title":"Neural networks"},{"issue":"1\u20133","key":"461_CR99","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K: Extreme learning machine: theory and applications. Neurocomputing 2006, 70(1-3):489-501.","journal-title":"Neurocomputing"},{"issue":"1","key":"461_CR100","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1109\/TNN.2009.2036259","volume":"21","author":"Y Miche","year":"2009","unstructured":"Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A: OP-ELM: optimally pruned extreme learning machine. IEEE transactions on neural networks 2009, 21(1):158-162.","journal-title":"IEEE transactions on neural networks"},{"key":"461_CR101","doi-asserted-by":"crossref","unstructured":"Dunn JC: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. 1973.","DOI":"10.1080\/01969727308546046"},{"issue":"1","key":"461_CR102","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan JA, Wong MA: Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society Series C (Applied Statistics) 1979, 28(1):100-108.","journal-title":"Journal of the Royal Statistical Society Series C (Applied Statistics)"},{"issue":"4","key":"461_CR103","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1177\/1544316718806421","volume":"42","author":"E Cuadrado-Godia","year":"2018","unstructured":"Cuadrado-Godia E, Srivastava SK, Saba L, Araki T, Suri HS, Giannopolulos A, Omerzu T, Laird J, Khanna NN, Mavrogeni S: Geometric total plaque area is an equally powerful phenotype compared with carotid intima-media thickness for stroke risk assessment: a deep learning approach. Journal for Vascular Ultrasound 2018, 42(4):162-188.","journal-title":"Journal for Vascular Ultrasound"},{"key":"461_CR104","doi-asserted-by":"crossref","unstructured":"Mainak Biswas LS, Shubhro Chakrabartty, Narender N Khanna, Hanjung Song,Harman S. Suri, Petros P. Sfikakis, Sophie Mavrogeni, Klaudija Viskovic, John R. Laird, Elisa Cuadrado-Godia, Andrew Nicolaides, Aditya Sharma, Vijay Viswanathan, Athanasios Protogerou, George Kitas, Gyan Pareek, Martin Miner, Jasjit S. Suri: Two-Stage Artificial Intelligence Model for Jointly Measurement of Atherosclerotic Wall Thickness and Plaque Burden in Carotid Ultrasound: A Screening Tool for Cardiovascular\/Stroke Risk Assessment. Computers in biology and medicine 2020.","DOI":"10.1016\/j.compbiomed.2020.103847"},{"key":"461_CR105","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.ejrad.2019.02.038","volume":"114","author":"L Saba","year":"2019","unstructured":"Saba L, Biswas M, Kuppili V, Cuadrado Godia E, Suri HS, Edla DR, Omerzu T, Laird JR, Khanna NN, Mavrogeni S et al: The present and future of deep learning in radiology. European Journal of Radiology 2019, 114:14-24.","journal-title":"European Journal of Radiology"},{"issue":"5","key":"461_CR106","first-page":"5947","volume":"4","author":"GE Hinton","year":"2009","unstructured":"Hinton GE: Deep belief networks. Scholarpedia 2009, 4(5):5947.","journal-title":"Deep belief networks. Scholarpedia"},{"key":"461_CR107","unstructured":"Baldi P: Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML workshop on unsupervised and transfer learning: 2012. 37\u201349."},{"key":"461_CR108","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition: 2016. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"461_CR109","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition: 2015. 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"461_CR110","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.neucom.2014.09.066","volume":"151","author":"R-M Mench\u00f3n-Lara","year":"2015","unstructured":"Mench\u00f3n-Lara R-M, Sancho-G\u00f3mez J-L: Fully automatic segmentation of ultrasound common carotid artery images based on machine learning. Neurocomputing 2015, 151:161-167.","journal-title":"Neurocomputing"},{"key":"461_CR111","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.compbiomed.2018.05.014","volume":"98","author":"M Biswas","year":"2018","unstructured":"Biswas M, Kuppili V, Araki T, Edla DR, Godia EC, Saba L, Suri HS, Omerzu T, Laird JR, Khanna NN: Deep learning strategy for accurate carotid intima-media thickness measurement: an ultrasound study on Japanese diabetic cohort. Computers in biology and medicine 2018, 98:100-117.","journal-title":"Computers in biology and medicine"},{"key":"461_CR112","doi-asserted-by":"publisher","first-page":"101784","DOI":"10.1016\/j.artmed.2019.101784","volume":"103","author":"Vila M del Mar","year":"2020","unstructured":"del Mar Vila M, Remeseiro B, Grau M, Elosua R, Betriu \u00c0, Fernandez-Giraldez E, Igual L: Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation. Artificial Intelligence in Medicine 2020, 103:101784.","journal-title":"Artificial Intelligence in Medicine"},{"key":"461_CR113","doi-asserted-by":"crossref","unstructured":"Molinari F, Meiburger KM, Saba L, Acharya UR, Famiglietti L, Georgiou N, Nicolaides A, Mamidi RS, Kuper H, Suri JS: Automated carotid IMT measurement and its validation in low contrast ultrasound database of 885 patient Indian population epidemiological study: results of AtheroEdge\u00ae software. In: Multi-modality atherosclerosis imaging and diagnosis. Springer; 2014: 209\u2013219.","DOI":"10.1007\/978-1-4614-7425-8_17"},{"issue":"9","key":"461_CR114","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1016\/j.compbiomed.2013.06.012","volume":"43","author":"L Saba","year":"2013","unstructured":"Saba L, Molinari F, Meiburger KM, Acharya UR, Nicolaides A, Suri JS: Inter-and intra-observer variability analysis of completely automated cIMT measurement software (AtheroEdge\u2122) and its benchmarking against commercial ultrasound scanner and expert Readers. Computers in Biology and Medicine 2013, 43(9):1261-1272.","journal-title":"Computers in Biology and Medicine"},{"issue":"1","key":"461_CR115","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s100440050005","volume":"3","author":"JS Suri","year":"2000","unstructured":"Suri JS, Haralick RM, Sheehan FH: Greedy algorithm for error correction in automatically produced boundaries from low contrast ventriculograms. Pattern Analysis & Applications 2000, 3(1):39-60.","journal-title":"Pattern Analysis & Applications"},{"key":"461_CR116","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.cmpb.2017.12.016","volume":"155","author":"M Biswas","year":"2018","unstructured":"Biswas M, Kuppili V, Edla DR, Suri HS, Saba L, Marinhoe RT, Sanches JM, Suri JS: Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Computer methods and programs in biomedicine 2018, 155:165-177.","journal-title":"Computer methods and programs in biomedicine"},{"key":"461_CR117","first-page":"165","volume":"155","author":"M Biswas","year":"2018","unstructured":"Biswas M, Kuppili V, Edla DR, Suri HS, Saba L, Marinhoe RT, Sanches JM, Suri JSJCm, biomedicine pi: Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. 2018, 155:165-177.","journal-title":"Suri JSJCm, biomedicine pi: Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm."},{"issue":"4","key":"461_CR118","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1109\/TMI.2014.2372784","volume":"34","author":"DD Carvalho","year":"2014","unstructured":"Carvalho DD, Akkus Z, van den Oord SC, Schinkel AF, van der Steen AF, Niessen WJ, Bosch JG, Klein S: Lumen segmentation and motion estimation in B-mode and contrast-enhanced ultrasound images of the carotid artery in patients with atherosclerotic plaque. IEEE transactions on medical imaging 2014, 34(4):983-993.","journal-title":"IEEE transactions on medical imaging"},{"key":"461_CR119","doi-asserted-by":"crossref","unstructured":"Gastounioti A, Golemati S, Stoitsis J, Nikita K: Kalman-filter-based block matching for arterial wall motion estimation from B-mode ultrasound. In: 2010 IEEE International Conference on Imaging Systems and Techniques: 2010. IEEE: 234\u2013239.","DOI":"10.1109\/IST.2010.5548454"},{"issue":"1","key":"461_CR120","first-page":"158","volume":"60","author":"DE Ilea","year":"2012","unstructured":"Ilea DE, Duffy C, Kavanagh L, Stanton A, Whelan PF: Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery. IEEE transactions on ultrasonics, ferroelectrics, and frequency control 2012, 60(1):158-177.","journal-title":"IEEE transactions on ultrasonics, ferroelectrics, and frequency control"},{"issue":"2","key":"461_CR121","first-page":"255","volume":"4","author":"T Araki","year":"2014","unstructured":"Araki T, Ikeda N, Molinari F, Dey N, Acharjee SM, Saba L, Nicolaides A, Suri JSJJoMI, Informatics H: Effect of geometric-based coronary calcium volume as a feature along with its shape-based attributes for cardiological risk prediction from low contrast intravascular ultrasound. 2014, 4(2):255-261.","journal-title":"Informatics H: Effect of geometric-based coronary calcium volume as a feature along with its shape-based attributes for cardiological risk prediction from low contrast intravascular ultrasound."},{"key":"461_CR122","doi-asserted-by":"crossref","unstructured":"Jamthikar A, Gupta D, Khanna NN, Saba L, Araki T, Viskovic K, Suri HS, Gupta A, Mavrogeni S, Turk MJCd et al: A low-cost machine learning-based cardiovascular\/stroke risk assessment system: integration of conventional factors with image phenotypes. 2019, 9(5):420.","DOI":"10.21037\/cdt.2019.09.03"},{"issue":"3","key":"461_CR123","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.cmpb.2010.04.007","volume":"100","author":"F Molinari","year":"2010","unstructured":"Molinari F, Zeng G, Suri JS: A state of the art review on intima\u2013media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound. Computer methods and programs in biomedicine 2010, 100(3):201-221.","journal-title":"Computer methods and programs in biomedicine"},{"issue":"2","key":"461_CR124","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1109\/42.836372","volume":"19","author":"Q Liang","year":"2000","unstructured":"Liang Q, Wendelhag I, Wikstrand J, Gustavsson T: A multiscale dynamic programming procedure for boundary detection in ultrasonic artery images. IEEE Transactions on medical imaging 2000, 19(2):127-142.","journal-title":"IEEE Transactions on medical imaging"},{"issue":"3","key":"461_CR125","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.echo.2004.12.002","volume":"18","author":"JH Stein","year":"2005","unstructured":"Stein JH, Korcarz CE, Mays ME, Douglas PS, Palta M, Zhang H, LeCaire T, Paine D, Gustafson D, Fan L: A semiautomated ultrasound border detection program that facilitates clinical measurement of ultrasound carotid intima-media thickness. Journal of the American Society of Echocardiography 2005, 18(3):244-251.","journal-title":"Journal of the American Society of Echocardiography"},{"issue":"9","key":"461_CR126","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.7863\/jum.2008.27.9.1353","volume":"27","author":"F Faita","year":"2008","unstructured":"Faita F, Gemignani V, Bianchini E, Giannarelli C, Ghiadoni L, Demi M: Real\u2010time measurement system for evaluation of the carotid intima\u2010media thickness with a robust edge operator. Journal of ultrasound in medicine 2008, 27(9):1353-1361.","journal-title":"Journal of ultrasound in medicine"},{"issue":"8","key":"461_CR127","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1007\/s11517-011-0781-8","volume":"49","author":"F Molinari","year":"2011","unstructured":"Molinari F, Acharya UR, Zeng G, Meiburger KM, Suri JS: Completely automated robust edge snapper for carotid ultrasound IMT measurement on a multi-institutional database of 300 images. Medical & biological engineering & computing 2011, 49(8):935-945.","journal-title":"Medical & biological engineering & computing"},{"issue":"6","key":"461_CR128","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1007\/s10278-011-9375-0","volume":"24","author":"F Molinari","year":"2011","unstructured":"Molinari F, Meiburger KM, Zeng G, Nicolaides A, Suri JS: CAUDLES-EF: carotid automated ultrasound double line extraction system using edge flow. Journal of digital imaging 2011, 24(6):1059-1077.","journal-title":"Journal of digital imaging"},{"key":"461_CR129","doi-asserted-by":"crossref","unstructured":"Molinari F, Suri JS: Automated measurement of carotid artery intima-media thickness. In: Ultrasound and Carotid Bifurcation Atherosclerosis. Springer; 2011: 177\u2013192.","DOI":"10.1007\/978-1-84882-688-5_11"},{"key":"461_CR130","unstructured":"Gutierrez MA, Pilon PE, Lage S, Kopel L, Carvalho R, Furuie S: Automatic measurement of carotid diameter and wall thickness in ultrasound images. In: Computers in Cardiology: 2002. IEEE: 359\u2013362."},{"issue":"04","key":"461_CR131","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1142\/S0219519409003115","volume":"9","author":"F Molinari","year":"2009","unstructured":"Molinari F, Liboni W, Giustetto P, Badalamenti S, Suri JS: Automatic computer-based tracings (ACT) in longitudinal 2-D ultrasound images using different scanners. Journal of Mechanics in Medicine and Biology 2009, 9(04):481-505.","journal-title":"Journal of Mechanics in Medicine and Biology"},{"key":"461_CR132","doi-asserted-by":"crossref","unstructured":"Molinari F, Acharya UR, Saba L, Nicolaides A, Suri JS: Hypothesis Validation of Far Wall Brightness in Carotid Artery Ultrasound for Feature-Based IMT Measurement Using a Combination of Level Set Segmentation and Registration. In: Multi-Modality Atherosclerosis Imaging and Diagnosis. Springer; 2014: 255\u2013267.","DOI":"10.1007\/978-1-4614-7425-8_21"},{"issue":"2","key":"461_CR133","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1111\/echo.14242","volume":"36","author":"NN Khanna","year":"2019","unstructured":"Khanna NN, Jamthikar AD, Araki T, Gupta D, Piga M, Saba L, Carcassi C, Nicolaides A, Laird JR, Suri HS: Nonlinear model for the carotid artery disease 10\u2010year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study. Echocardiography 2019, 36(2):345-361.","journal-title":"Echocardiography"},{"key":"461_CR134","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.compbiomed.2019.03.020","volume":"108","author":"E Cuadrado-Godia","year":"2019","unstructured":"Cuadrado-Godia E, Jamthikar AD, Gupta D, Khanna NN, Araki T, Maniruzzaman M, Saba L, Nicolaides A, Sharma A, Omerzu T: Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach. Computers in biology and medicine 2019, 108:182-195.","journal-title":"Computers in biology and medicine"},{"key":"461_CR135","doi-asserted-by":"crossref","unstructured":"Jamthikar A, Gupta D, Saba L, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Sattar N, Johri AM, Pareek GJCiB et al: Artificial Intelligence Framework for Predictive Cardiovascular and Stroke Risk Assessment Models: A Narrative Review of Integrated Approaches using Carotid Ultrasound. 2020:104043.","DOI":"10.1016\/j.compbiomed.2020.104043"},{"key":"461_CR136","doi-asserted-by":"crossref","unstructured":"Jamthikar AD, Puvvula A, Gupta D, Johri AM, Nambi V, Khanna NN, Saba L, Mavrogeni S, Laird JR, Pareek G: Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review. International Angiology: a Journal of the International Union of Angiology 2020.","DOI":"10.23736\/S0392-9590.20.04538-1"},{"key":"461_CR137","doi-asserted-by":"crossref","unstructured":"Narayanan R, Kurhanewicz J, Shinohara K, Crawford ED, Simoneau A, Suri JS: MRI-ultrasound registration for targeted prostate biopsy. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro: 2009. IEEE: 991\u2013994.","DOI":"10.1109\/ISBI.2009.5193221"},{"issue":"5","key":"461_CR138","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1016\/j.ihj.2018.01.024","volume":"70","author":"L Saba","year":"2018","unstructured":"Saba L, Banchhor SK, Araki T, Viskovic K, Londhe ND, Laird JR, Suri HS, Suri JS: Intra-and inter-operator reproducibility of automated cloud-based carotid lumen diameter ultrasound measurement. Indian Heart Journal 2018, 70(5):649-664.","journal-title":"Indian Heart Journal"},{"key":"461_CR139","doi-asserted-by":"crossref","unstructured":"Saba L, Than JCM, Noor NM, Rijal OM, Kassim RM, Yunus A, Ng CR, Suri JS: Inter-observer Variability Analysis of Automatic Lung Delineation in Normal and Disease Patients. Journal of Medical Systems 2016, 40(6).","DOI":"10.1007\/s10916-016-0504-7"},{"key":"461_CR140","first-page":"384","volume":"113","author":"S Zhang","year":"2005","unstructured":"Zhang S, Suri JS, Salvado O, Chen Y, Wacker FK, Wilson DL, Duerk JL, Lewin JS: Inter-and Intra-Observer Variability Assessment of in Vivo Carotid Plaque Burden Quantification Using Multi-Contrast Dark Blood MR Images. Studies in health technology and informatics 2005, 113:384-393.","journal-title":"Studies in health technology and informatics"},{"issue":"2","key":"461_CR141","first-page":"543","volume":"57","author":"M Biswas","year":"2019","unstructured":"Biswas M, Kuppili V, Saba L, Edla DR, Suri HS, Sharma A, Cuadrado-Godia E, Laird JR, Nicolaides A, Suri JSJM et al: Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk. 2019, 57(2):543-564.","journal-title":"Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk."},{"key":"461_CR142","doi-asserted-by":"publisher","first-page":"103847","DOI":"10.1016\/j.compbiomed.2020.103847","volume":"123","author":"M Biswas","year":"2020","unstructured":"Biswas M, Saba L, Chakrabartty S, Khanna NN, Song H, Suri HS, Sfikakis PP, Mavrogeni S, Viskovic K, Laird JR: Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular\/stroke risk assessment. Computers in Biology and Medicine 2020, 123:103847.","journal-title":"Computers in Biology and Medicine"},{"issue":"4","key":"461_CR143","doi-asserted-by":"publisher","first-page":"919","DOI":"10.21037\/cdt.2020.01.07","volume":"10","author":"A Jamthikar","year":"2020","unstructured":"Jamthikar A, Gupta D, Saba L, Khanna NN, Araki T, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M: Cardiovascular\/stroke risk predictive calculators: a comparison between statistical and machine learning models. Cardiovascular Diagnosis and Therapy 2020, 10(4):919.","journal-title":"Cardiovascular Diagnosis and Therapy"},{"issue":"4","key":"461_CR144","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.ihj.2020.06.004","volume":"72","author":"A Jamthikar","year":"2020","unstructured":"Jamthikar A, Gupta D, Khanna NN, Saba L, Laird JR, Suri JS: Cardiovascular\/stroke risk prevention: a new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors. Indian heart journal 2020, 72(4):258-264.","journal-title":"Indian heart journal"},{"key":"461_CR145","first-page":"1132","volume":"25","author":"V Viswanathan","year":"2020","unstructured":"Viswanathan V, Jamthikar AD, Gupta D, Shanu N, Puvvula A, Khanna NN, Saba L, Omerzum T, Viskovic K, Mavrogeni SJFiB: Low-cost preventive screening using carotid ultrasound in patients with diabetes. 2020, 25:1132-1171.","journal-title":"Mavrogeni SJFiB: Low-cost preventive screening using carotid ultrasound in patients with diabetes."},{"issue":"11","key":"461_CR146","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1016\/S0895-4356(03)00177-X","volume":"56","author":"AS Glas","year":"2003","unstructured":"Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM: The diagnostic odds ratio: a single indicator of test performance. Journal of clinical epidemiology 2003, 56(11):1129-1135.","journal-title":"Journal of clinical epidemiology"},{"issue":"1","key":"461_CR147","doi-asserted-by":"publisher","first-page":"55","DOI":"10.4103\/0974-7788.59946","volume":"1","author":"P Kadam","year":"2010","unstructured":"Kadam P, Bhalerao S: Sample size calculation. International journal of Ayurveda research 2010, 1(1):55.","journal-title":"International journal of Ayurveda research"},{"key":"461_CR148","unstructured":"Hadjis S, Zhang C, Mitliagkas I, Iter D, R\u00e9 C: Omnivore: An optimizer for multi-device deep learning on cpus and gpus. arXiv preprint\u00a0arXiv:160604487\u00a02016."},{"key":"461_CR149","unstructured":"Chen T, Li M, Li Y, Lin M, Wang N, Wang M, Xiao T, Xu B, Zhang C, Zhang Z: Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems.\u00a0arXiv preprint arXiv:151201274\u00a02015."},{"key":"461_CR150","unstructured":"Ooi BC, Tan K-L, Wang S, Wang W, Cai Q, Chen G, Gao J, Luo Z, Tung AK, Wang Y: SINGA: A distributed deep learning platform. In: Proceedings of the 23rd ACM international conference on Multimedia: 2015. 685\u2013688."},{"key":"461_CR151","first-page":"166","volume":"11","author":"L Saba","year":"2019","unstructured":"Saba L, Tiwari A, Biswas M, Gupta SK, Godia-Cuadrado E, Chaturvedi A, Turk M, Suri HS, Orru S, Sanches JM: Wilson's disease: A new perspective review on its genetics, diagnosis and treatment. Frontiers in bioscience (Elite edition) 2019, 11:166-185.","journal-title":"Frontiers in bioscience (Elite edition)"},{"issue":"1","key":"461_CR152","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3390\/cancers11010111","volume":"11","author":"GS Tandel","year":"2019","unstructured":"Tandel GS, Biswas M, Kakde OG, Tiwari A, Suri HS, Turk M, Laird JR, Asare CK, Ankrah AA, Khanna N: A review on a deep learning perspective in brain cancer classification. Cancers 2019, 11(1):111.","journal-title":"Cancers"},{"issue":"9","key":"461_CR153","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s11883-015-0529-2","volume":"17","author":"AM Sharma","year":"2015","unstructured":"Sharma AM, Gupta A, Kumar PK, Rajan J, Saba L, Nobutaka I, Laird JR, Nicolades A, Suri JS: A review on carotid ultrasound atherosclerotic tissue characterization and stroke risk stratification in machine learning framework. Current atherosclerosis reports 2015, 17(9):55.","journal-title":"Current atherosclerosis reports"},{"key":"461_CR154","unstructured":"Flach P: The many faces of ROC analysis in machine learning. ICML Tutorial 2004."},{"issue":"1\u20132","key":"461_CR155","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/S0004-3702(01)00125-4","volume":"131","author":"PA Flach","year":"2001","unstructured":"Flach PA: On the state of the art in machine learning: A personal review. Artificial Intelligence 2001, 131(1-2):199-222.","journal-title":"Artificial Intelligence"},{"issue":"1","key":"461_CR156","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P: Nearest neighbor pattern classification. IEEE transactions on information theory 1967, 13(1):21-27.","journal-title":"IEEE transactions on information theory"},{"issue":"3","key":"461_CR157","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V: Support-vector networks. Machine learning 1995, 20(3):273-297.","journal-title":"Machine learning"},{"key":"461_CR158","doi-asserted-by":"crossref","unstructured":"Majumder M: Artificial Neural Network. In: Impact of Urbanization on Water Shortage in Face of Climatic Aberrations. Springer; 2015: 49\u201354.","DOI":"10.1007\/978-981-4560-73-3_3"},{"key":"461_CR159","volume-title":"Gibbs sampling for the uninitiated","author":"P Resnik","year":"2010","unstructured":"Resnik P, Hardisty E: Gibbs sampling for the uninitiated. In.: Maryland Univ College Park Inst for Advanced Computer Studies; 2010."},{"issue":"1","key":"461_CR160","first-page":"81","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR: Induction of decision trees. Machine learning 1986, 1(1):81-106.","journal-title":"Machine learning"},{"key":"461_CR161","unstructured":"LeCun Y, Touresky D, Hinton G, Sejnowski T: A theoretical framework for back-propagation. In: Proceedings of the 1988 connectionist models summer school: 1988. CMU, Pittsburgh, Pa: Morgan Kaufmann: 21\u201328."},{"issue":"3","key":"461_CR162","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1162\/neco.1989.1.3.295","volume":"1","author":"HB Barlow","year":"1989","unstructured":"Barlow HB: Unsupervised learning. Neural computation 1989, 1(3):295-311.","journal-title":"Neural computation"},{"key":"461_CR163","unstructured":"Alsabti K, Ranka S, Singh V: An efficient k-means clustering algorithm. 1997."}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00461-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-021-00461-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00461-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T19:11:51Z","timestamp":1622661111000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-021-00461-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,2]]},"references-count":163,"alternative-id":["461"],"URL":"https:\/\/doi.org\/10.1007\/s10278-021-00461-2","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,2]]},"assertion":[{"value":"1 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}