Automated Techniques for Vessel Detection and Segmentation in Cardiovascular Images

  • Kristen M. Meiburger
  • Cristina Caresio
  • Massimo Salvi
  • Filippo MolinariEmail author
Part of the Series in BioEngineering book series (SERBIOENG)


Imaging plays a fundamental role in the assessment of the cardiovascular system and it aids clinicians and researchers in several fields and applications. As examples, from a clinical point of view, imaging is fundamental in the planning of surgical structural interventions [1], in functional studies (such as myocardial perfusion) [2], and in the monitoring of post-surgical patients [3]. In research, examples of imaging applications are the study of inflammatory processes [4], of atherosclerosis [5], and of biomarkers targeted for specific diseases [6, 7, 8].


  1. 1.
    Carminati M, Agnifili M, Arcidiacono C, Brambilla N, Bussadori C, Butera G et al (2013) Role of imaging in interventions on structural heart disease. Expert Rev Cardiovasc Ther 11(12):1659–1676Google Scholar
  2. 2.
    Qayyum AA, Kastrup J (2015) Measuring myocardial perfusion: the role of PET. MRI and CT Clin Radiol 70(6):576–584Google Scholar
  3. 3.
    Ferguson TB, Buch AN (2016) Improving quality and outcomes of coronary artery bypass grafting procedures. Expert Rev Cardiovasc Ther 14(5):617–631Google Scholar
  4. 4.
    Castañeda S, Nurmohamed MT, González-Gay MA (2016) Cardiovascular disease in inflammatory rheumatic diseases. Best Pract Res Clin Rheumatol 30(5):851–869Google Scholar
  5. 5.
    Ladeiras-Lopes R, Agewall S, Tawakol A, Staels B, Stein E, Mentz RJ et al (2015) Atherosclerosis: recent trials, new targets and future directions. Int J Cardiol 192:72–81Google Scholar
  6. 6.
    Jalalzadeh H, Indrakusuma R, Planken RN, Legemate DA, Koelemay MJW, Balm R (2016) Inflammation as a predictor of abdominal aortic aneurysm growth and rupture: a systematic review of imaging biomarkers. Eur J Vasc Endovasc Surg 52(3):333–342Google Scholar
  7. 7.
    Sharifi M, Rakhit RD, Humphries SE, Nair D (2016) Cardiovascular risk stratification in familial hypercholesterolaemia. Heart 102(13):1003–1008Google Scholar
  8. 8.
    Tehrani DM, Wong ND (2016) Integrating biomarkers and imaging for cardiovascular disease risk assessment in diabetes. Curr Cardiol Rep 18(11):105Google Scholar
  9. 9.
    Di Carli MF, Geva T, Davidoff R (2016) The future of cardiovascular imaging. Circulation 133(25)Google Scholar
  10. 10.
    Pison L, Proclemer A, Bongiorni MG, Marinskis G, Hernandez-Madrid A, Blomstrom-Lundqvist C et al (2013) Imaging techniques in electrophysiology and implantable device procedures: results of the European heart rhythm association survey. Europace 15(9):1333–1336Google Scholar
  11. 11.
    Blankstein R (2012) Introduction to noninvasive cardiac imaging. Circulation 125(3)Google Scholar
  12. 12.
    Cardim N, Galderisi M, Edvardsen T, Plein S, Popescu BA, D’Andrea A et al (2015) Role of multimodality cardiac imaging in the management of patients with hypertrophic cardiomyopathy: an expert consensus of the European association of cardiovascular imaging endorsed by the Saudi heart association. Eur Heart J Cardiovasc Imaging 16(3)Google Scholar
  13. 13.
    McAteer MA, Choudhury RP (2015) Noninvasive molecular imaging of mouse atherosclerosis. Methods Mol Biol 1339:61–83Google Scholar
  14. 14.
    Labropoulos N, Leon LR, Brewster LP, Pryor L, Tiongson J, Kang SS et al (2005) Are your arteries older than your age? Eur J Vasc Endovasc Surg 30(6):588–596Google Scholar
  15. 15.
    Kianoush S, Al Rifai M, Whelton SP, Shaya GE, Bush AL, Graham G et al (2016) Stratifying cardiovascular risk in diabetes: the role of diabetes-related clinical characteristics and imaging. J Diabetes Complicat 30(7):1408–1415Google Scholar
  16. 16.
    Laking GR, West C, Buckley DL, Matthews J, Price PM (2006) Imaging vascular physiology to monitor cancer treatment. Crit Rev Oncol Hematol 58(2):95–113Google Scholar
  17. 17.
    Dake MD (2012) Chronic cerebrospinal venous insufficiency and multiple sclerosis: history and background. Tech Vasc Interv Radiol 15(2):94–100Google Scholar
  18. 18.
    Buzug TM (2008) Computed tomography: from photon statistics to modern cone-beam CT. SpringerGoogle Scholar
  19. 19.
    Krishnamurthy R, Cheong B, Muthupillai R (2014) Tools for cardiovascular magnetic resonance imaging. Cardiovasc Diagn Ther 4(2):104–25Google Scholar
  20. 20.
    Molinari F, Krishnamurthi G, Acharya UR, Sree SV, Saba L, Nicolaides A et al (2012) 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 Trans Instrum Meas 61(4):1054–1063Google Scholar
  21. 21.
    Rocha R, Campilho A, Silva J, Azevedo E, Santos R (2010) Segmentation of the carotid intima-media region in B-mode ultrasound images. Image Vis Comput 28(4):614–625Google Scholar
  22. 22.
    Touboul P-J, Hennerici MG, Meairs S, Adams H, Amarenco P, Bornstein N et al (2012) Mannheim carotid intima-media thickness and plaque consensus (2004–2006–2011). In: An update on behalf of the advisory board of the 3rd, 4th and 5th watching the risk symposia, at the 13th, 15th and 20th European stroke conferences, Mannheim, Germany, 2004. Cerebrovasc Dis 34(4):290–296Google Scholar
  23. 23.
    van’t Klooster R, de Koning PJH, Dehnavi RA, Tamsma JT, de Roos A, Reiber JHC et al (2012) Automatic lumen and outer wall segmentation of the carotid artery using deformable three-dimensional models in MR angiography and vessel wall images. J Magn Reson Imaging 35(1):156–65Google Scholar
  24. 24.
    Molinari F, Caresio C, Acharya UR, Mookiah MRK, Minetto MA (2015) Advances in quantitative muscle ultrasonography using texture analysis of ultrasound images. Ultrasound Med Biol [Internet] 41(9):2520–32. Accessed 26 Nov 2015Google Scholar
  25. 25.
    Acharya RU, Faust O, Alvin APC, Sree SV, Molinari F, Saba L et al (2012) Symptomatic vs. asymptomatic plaque classification in carotid ultrasound. J Med Syst 36(3):1861–1871Google Scholar
  26. 26.
    Kakkos SK, Griffin MB, Nicolaides AN, Kyriacou E, Sabetai MM, Tegos T et al (2013) The size of juxtaluminal hypoechoic area in ultrasound images of asymptomatic carotid plaques predicts the occurrence of stroke. J Vasc Surg 57(3):609–618Google Scholar
  27. 27.
    Molinari F, Meiburger KM, Zeng G, Acharya UR, Liboni W, Nicolaides A et al (2012) Carotid artery recognition system: a comparison of three automated paradigms for ultrasound images. Med Phys 39(1):378Google Scholar
  28. 28.
    Tegos TJ, Mavrophoros D, Sabetai MM, Elatrozy TS, Dhanjil S, Karapataki M et al (2001) Types of neurovascular symptoms and carotid plaque ultrasonic textural characteristics. J Ultrasound Med 20(2):113–21Google Scholar
  29. 29.
    Mahapatra D (2014) Automatic cardiac segmentation using semantic information from random forests. J Digit Imaging 27(6):794–804Google Scholar
  30. 30.
    Rosati S, Balestra G, Molinari F, Rajendra Acharya U, Suri JS (2014) A selection and reduction approach for the optimization of ultrasound carotid artery images segmentation. Mach Learn Healthc Inf 309–32Google Scholar
  31. 31.
    Molinari F, Zeng G, Suri JS (2010) Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement. IEEE Trans Ultrason Ferroelectr Freq Control 57(5):1112–1124Google Scholar
  32. 32.
    Molinari F, Liboni W, Giustetto P, Badalamenti S, Suri JS (2009) Automatic computer-based tracings (ACT) in longitudinal 2-D ultrasound images using different scanners. J Mech Med Biol [Internet] 9(4):481–505. Accessed 6 May 2016Google Scholar
  33. 33.
    Sifakis EG, Golemati S (2014) Robust carotid artery recognition in longitudinal B-mode ultrasound images. IEEE Trans Image Process 23(9):3762–3772MathSciNetzbMATHGoogle Scholar
  34. 34.
    Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. In: 2005 IEEE computer society conference on computer vision pattern recognition. IEEE, pp 430–436Google Scholar
  35. 35.
    Saba L, Gao H, Raz E, Sree SV, Mannelli L, Tallapally N et al (2014) Semiautomated analysis of carotid artery wall thickness in MRI. J Magn Reson Imaging 39(6):1457–1467Google Scholar
  36. 36.
    Boussel L, Serusclat A, Skilton M, Vincent F, Bernard S, Moulin P et al (2007) The reliability of high resolution MRI in the measurement of early stage carotid wall thickening. J Cardiovasc Magn Reson 9(5):771–776Google Scholar
  37. 37.
    Underhill HR, Kerwin WS, Hatsukami TS, Yuan C (2006) Automated measurement of mean wall thickness in the common carotid artery by MRI: a comparison to intima-media thickness by B-mode ultrasound. J Magn Reson Imaging 24(2):379–387Google Scholar
  38. 38.
    Nael K, Krishnam M, Nael A, Ton A, Ruehm SG, Finn JP (2008) Peripheral contrast-enhanced MR angiography at 3.0T, improved spatial resolution and low dose contrast: initial clinical experience. Eur Radiol 18(12):2893–2900Google Scholar
  39. 39.
    Nowinski WL, Puspitasaari F, Volkau I, Marchenko Y, Knopp MV (2013) Comparison of magnetic resonance angiography scans on 1.5, 3, and 7 tesla units: a quantitative study of 3-dimensional cerebrovasculature. J Neuroimaging 23(1):86–95Google Scholar
  40. 40.
    Loizou CP, Pattichis CS, Nicolaides AN, Pantziaris M (2009) Manual and automated media and intima thickness measurements of the common carotid artery. IEEE Trans Ultrason Ferroelectr Freq Control 56(5):983–994Google Scholar
  41. 41.
    Pignoli P, Longo T (1988) Evaluation of atherosclerosis with B-mode ultrasound imaging. J Nucl Med Allied Sci 32(3):166–173Google Scholar
  42. 42.
    Molinari F, Rajendra Acharya U, Zeng G, Meiburger KM, Suri JS (2011) Completely automated robust edge snapper for carotid ultrasound IMT measurement on a multi-institutional database of 300 images. Med Biol Eng Comput [Internet] 49(8):935–45. Assessed 22 Sept 2015Google Scholar
  43. 43.
    Liguori C, Paolillo A, Pietrosanto A (2001) An automatic measurement system for the evaluation of carotid intima-media thickness. IEEE Transactions on instrumentation and measurement 50(6):1684–1691Google Scholar
  44. 44.
    Faita F, Gemignani V, Bianchini E, Giannarelli C, Ghiadoni L, Demi M (2008) Real-time measurement system for evaluation of the carotid intima-media thickness with a robust edge operator. J Ultrasound Med 27(9):1353–1361Google Scholar
  45. 45.
    Wendelhag I, Gustavsson T, Suurküla M, Berglund G, Wikstrand J (1991) Ultrasound measurement of wall thickness in the carotid artery: fundamental principles and description of a computerized analysing system. Clin Physiol 11(6):565–577Google Scholar
  46. 46.
    Wendelhag I, Liang Q, Gustavsson T, Wikstrand J (1997) A new automated computerized analyzing system simplifies readings and reduces the variability in ultrasound measurement of intima-media thickness. Stroke 28(11):2195–2200Google Scholar
  47. 47.
    Quan Liang Q, Wendelhag I, Wikstrand J, Gustavsson T (2000) A multiscale dynamic programming procedure for boundary detection in ultrasonic artery images. IEEE Trans Med Imaging 19(2):127–142Google Scholar
  48. 48.
    Williams DJ, Shah MA (1992) Fast algorithm for active contours and curvature estimation. CVGIP Image Underst 55(1):14–26zbMATHGoogle Scholar
  49. 49.
    Loizou CP, Pattichis CS, Pantziaris M, Tyllis T, Nicolaides A (2007) Snakes based segmentation of the common carotid artery intima media. Med Biol Eng Comput 45(1):35–49Google Scholar
  50. 50.
    Loizou CP, Nicolaides A, Kyriacou E, Georghiou N, Griffin M, Pattichis CS (2015) A Comparison of ultrasound intima-media thickness measurements of the left and right common carotid artery. IEEE J Transl Eng Health Med 3:1–10Google Scholar
  51. 51.
    Loizou CP, Pattichis CS, Pantziaris M, Nicolaides A (2007) An integrated system for the segmentation of atherosclerotic carotid plaque. IEEE Trans Inf Technol Biomed 11(6):661–667Google Scholar
  52. 52.
    Molinari F, Meiburger KM, Saba L, Zeng G, Acharya UR, Ledda M et al (2012) Fully automated dual-snake formulation for carotid intima-media thickness measurement. J Ultrasound Med 31(7):1123–1136Google Scholar
  53. 53.
    Molinari F, Meiburger KM, Saba L, Acharya U Rajendra, Ledda M, Nicolaides A et al (2012) Constrained snake vs. conventional snake for carotid ultrasound automated IMT measurements on multi-center data sets. Ultrasonics 52(7):949–961Google Scholar
  54. 54.
    Paul VCH (1960) Method and means for recognizing complex patternsGoogle Scholar
  55. 55.
    Golemati S, Stoitsis J, Sifakis EG, Balkizas T, Nikita KS (2007) Using the hough transform to segment ultrasound images of longitudinal and transverse sections of the carotid artery. Ultrasound Med Biol 33(12):1918–1932Google Scholar
  56. 56.
    Golemati S, Tegos TJ, Sassano A, Nikita KS, Nicolaides AN (2004) Echogenicity of B-mode sonographic images of the carotid artery: work in progress. J Ultrasound Med 23(5):659–669Google Scholar
  57. 57.
    Veronese E, Tarroni G, Visentin S, Cosmi E, Linguraru MG, Grisan E (2014) Estimation of prenatal aorta intima-media thickness from ultrasound examination. Phys Med Biol 59(21):6355–6371Google Scholar
  58. 58.
    Shankar PM (2003) A compound scattering pdf for the ultrasonic echo envelope and its relationship to K and Nakagami distributions. IEEE Trans Ultrason Ferroelectr Freq Control 50(3):339–343MathSciNetGoogle Scholar
  59. 59.
    Destrempes F, Meunier J, Giroux M-F, Soulez G, Cloutier G (2009) Segmentation in ultrasonic B-mode images of healthy carotid arteries using mixtures of nakagami distributions and stochastic optimization. IEEE Trans Med Imaging 28(2):215–229Google Scholar
  60. 60.
    Rosati S, Meiburger KM, Balestra G, Acharya UR, Molinari F (2016) Carotid wall measurement and assessment based on pixel-based and local texture descriptors. J Mech Med Biol [Internet] 16(1):1640006. Accessed 16 Jan 2017Google Scholar
  61. 61.
    Delsanto S, Molinari F, Giustetto P, Liboni W, Badalamenti S, Suri JS (2007) Characterization of a completely user-independent algorithm for carotid artery segmentation in 2-D ultrasound images. IEEE Trans Instrum Meas [Internet] 56(4):1265–74. Accessed 22 Sept 2015Google Scholar
  62. 62.
    Pazinato D, Stein B, Almeida W, Werneck R, Mendes Junior P, Penatti O et al (2014) Pixel-level tissue classification for ultrasound images. IEEE J Biomed Health Inform [Internet]. Accessed 28 Nov 2015
  63. 63.
    Meiburger KM, Rosati S, Balestra G, Acharya UR, Molinari F (2016) Ultrasound B-mode descriptors and their association to age and automated IMT and IMT variability. J Mech Med Biol 16(1):1640007Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Kristen M. Meiburger
    • 1
  • Cristina Caresio
    • 1
  • Massimo Salvi
    • 1
  • Filippo Molinari
    • 1
    Email author
  1. 1.Biolab, Department of Electronics and TelecommunicationsPolitecnico di TorinoTurinItaly

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