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Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach

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Abstract

Monitoring of cerebrovascular diseases via carotid ultrasound has started to become a routine. The measurement of image-based lumen diameter (LD) or inter-adventitial diameter (IAD) is a promising approach for quantification of the degree of stenosis. The manual measurements of LD/IAD are not reliable, subjective and slow. The curvature associated with the vessels along with non-uniformity in the plaque growth poses further challenges. This study uses a novel and generalized approach for automated LD and IAD measurement based on a combination of spatial transformation and scale-space. In this iterative procedure, the scale-space is first used to get the lumen axis which is then used with spatial image transformation paradigm to get a transformed image. The scale-space is then reapplied to retrieve the lumen region and boundary in the transformed framework. Then, inverse transformation is applied to display the results in original image framework. Two hundred and two patients’ left and right common carotid artery (404 carotid images) B-mode ultrasound images were retrospectively analyzed. The validation of our algorithm has done against the two manual expert tracings. The coefficient of correlation between the two manual tracings for LD was 0.98 (p < 0.0001) and 0.99 (p < 0.0001), respectively. The precision of merit between the manual expert tracings and the automated system was 97.7 and 98.7%, respectively. The experimental analysis demonstrated superior performance of the proposed method over conventional approaches. Several statistical tests demonstrated the stability and reliability of the automated system.

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References

  1. Araki T, Kumar KP, Suri HS et al (2016) Two automated techniques for carotid lumen diameter measurement: regional versus boundary approaches. J Med Syst 40(7):1–19

    Article  Google Scholar 

  2. Babaud J, Witkin AP, Baudin M et al (1986) Uniqueness of the Gaussian kernel for scale-space filtering. IEEE Trans Pattern Anal Mach Intell 8(1):26–33

    Article  CAS  PubMed  Google Scholar 

  3. Bharatha A, Hirose M, Hata N et al (2001) Evaluation of three-dimensional finite element-based deformable registration of pre-and intraoperative prostate imaging. Med Phys 28(12):2551–2560

    Article  CAS  PubMed  Google Scholar 

  4. Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327(8476):307–310

    Article  Google Scholar 

  5. Bots ML, Hoes AW, Koudstaal PJ et al (1997) Common carotid intima-media thickness and risk of stroke and myocardial infarction: the Rotterdam Study. Circulation 96(5):1432–1437

    Article  CAS  PubMed  Google Scholar 

  6. Bots ML, Grobbee DE, Hofman A et al (2005) Common carotid intima-media thickness and risk of acute myocardial infarction: the role of lumen diameter. Stroke 36(4):762–767

    Article  PubMed  Google Scholar 

  7. Box JF (1987) Guinness, Gosset, Fisher, and small samples. Stat Sci 2(1):45–52

    Article  Google Scholar 

  8. Bray R, Derpapas A, Fernando R et al (2016) Does the vaginal wall become thinner as prolapse grade increases? Int Urogynecol J. doi:10.1007/s00192-016-3150-1

    PubMed  PubMed Central  Google Scholar 

  9. Carvalho DD, Akkus Z, van den Oord SC et al (2015) Lumen segmentation and motion estimation in B-mode and contrast-enhanced ultrasound images of the carotid artery in patients with atherosclerotic plaque. IEEE Trans Med Imaging 34(4):983–993

    Article  PubMed  Google Scholar 

  10. Cheng HD, Li J (2003) Fuzzy homogeneity and scale-space approach to color image segmentation. Pattern Recognit 36(7):1545–1562

    Article  Google Scholar 

  11. Cinthio M, Jansson T, Eriksson A et al (2010) Evaluation of an algorithm for arterial lumen diameter measurements by means of ultrasound. Med Biol Eng Comput 48(11):1133–1140

    Article  PubMed  Google Scholar 

  12. Coupé P, Hellier P, Kervrann C et al (2009) Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans Image Process 18(10):2221–2229

    Article  PubMed  PubMed Central  Google Scholar 

  13. Delsanto S, Molinari F, Giustetto P et al (2007) Characterization of a completely user-independent algorithm for carotid artery segmentation in 2-D ultrasound images. IEEE Trans Instrum Meas 56(4):1265–1274

    Article  Google Scholar 

  14. Eigenbrodt ML, Sukhija R, Rose KM et al (2007) Common carotid artery wall thickness and external diameter as predictors of prevalent and incident cardiac events in a large population study. Cardiovasc Ultrasound 5(1):1–11

    Article  Google Scholar 

  15. Filardi V (2013) Carotid artery stenosis near a bifurcation investigated by fluid dynamic analyses. Neuroradiol J 26(4):439–453

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86–92

    Article  Google Scholar 

  17. Gamble G, Zorn J, Sanders G et al (1994) Estimation of arterial stiffness, compliance, and distensibility from M-mode ultrasound measurements of the common carotid artery. Stroke 25(1):11–16

    Article  CAS  PubMed  Google Scholar 

  18. Glagov S, Weisenberg E, Zarins CK et al (1987) Compensatory enlargement of human atherosclerotic coronary arteries. N Engl J Med 316(22):1371–1375

    Article  CAS  PubMed  Google Scholar 

  19. Golemati S, Stoitsis J, Sifakis EG et al (2007) Using the Hough transform to segment ultrasound images of longitudinal and transverse sections of the carotid artery. Ultrasound Med Biol 33(12):1918–1932

    Article  PubMed  Google Scholar 

  20. Grønholdt MLM, Nordestgaard BG, Schroeder TV et al (2001) Ultrasonic echolucent carotid plaques predict future strokes. Circulation 104(1):68–73

    Article  PubMed  Google Scholar 

  21. Gupta A, Nair S, Schweitzer AD et al (2012) Neuroimaging of cerebrovascular disease in the aging brain. Aging Dis 3(5):414–425

    PubMed  PubMed Central  Google Scholar 

  22. Gupta A, Kesavabhotla K, Baradaran H et al (2015) Plaque echolucency and stroke risk in asymptomatic carotid stenosis a systematic review and meta-analysis. Stroke 46(1):91–97

    Article  PubMed  Google Scholar 

  23. https://www.medcalc.org

  24. Hollander M, Wolfe DA, Chicken E (2013) Non-parametric statistical methods. Wiley, Hoboken

    Google Scholar 

  25. Ikeda N, Araki T, Dey N et al (2014) Automated and accurate carotid bulb detection, its verification and validation in low quality frozen frames and motion video. Int Angiol 33(6):573–589

    CAS  PubMed  Google Scholar 

  26. Ikeda N, Gupta A, Dey N et al (2015) Improved correlation between carotid and coronary atherosclerosis SYNTAX score using automated ultrasound carotid bulb plaque IMT measurement. Ultrasound Med Biol 41(5):1247–1262

    Article  PubMed  Google Scholar 

  27. Jaccard P (1912) The distribution of the flora in the alpine zone. New Phytol 11(2):37–50

    Article  Google Scholar 

  28. Jodas DS, Pereira AS, Tavares JMR (2016) A review of computational methods applied for identification and quantification of atherosclerotic plaques in images. Expert Syst Appl 46(2016):1–14

    Article  Google Scholar 

  29. Lamont D, Parker L, White M et al (2000) Risk of cardiovascular disease measured by carotid intima-media thickness at age 49–51: life course study. Br Med J 320(7230):273–278

    Article  CAS  Google Scholar 

  30. Loizou CP, Pattichis CS, Christodoulou CI et al (2005) Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery. IEEE Trans Ultrason Ferroelectr Freq Control 52(10):1653–1669

    Article  PubMed  Google Scholar 

  31. Loizou CP, Pattichis CS, Pantziaris M et al (2007) Snakes based segmentation of the common carotid artery intima media. Med Biol Eng Comput 45(1):35–49

    Article  CAS  PubMed  Google Scholar 

  32. Loizou CP, Kasparis T, Spyrou C et al (2013) Integrated system for the complete segmentation of the common carotid artery bifurcation in ultrasound images. Artif Intell Appl Innov 412(1):292–301

    Article  Google Scholar 

  33. Molinar F, Meiburger KM, Saba L et al (2012) Constrained snake vs. conventional snake for carotid ultrasound automated IMT measurements on multi-center data sets. Ultrasonics 52(7):949–961

    Article  Google Scholar 

  34. Molinari F, Zeng G, Suri JS (2010) An integrated approach to computer based automated tracing and its validation for 200 common carotid arterial wall ultrasound images. J Ultrasound Med 29(3):399–418

    Article  PubMed  Google Scholar 

  35. Molinari F, Zeng G, Suri JS (2010) A state of the art review on intima–media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound. Comput Methods Programs Biomed 100(3):201–221

    Article  PubMed  Google Scholar 

  36. Molinari F, Zeng G, Suri JS (2011) Inter-greedy technique for fusion of different segmentation strategies leading to high-performance carotid IMT measurement in ultrasound images. J Med Syst 35(5):905–919

    Article  PubMed  Google Scholar 

  37. Molinari F, Krishnamurthi G, Acharya UR 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):1054–1063

    Article  Google Scholar 

  38. Molinari F, Meiburger KM, Saba L et al (2012) Fully automated dual-snake formulation for carotid intima-media thickness measurement a new approach. J Ultrasound Med 31(7):1123–1136

    Article  PubMed  Google Scholar 

  39. Molinari F, Meiburger KM, Zeng G et al (2012) Automated carotid IMT measurement and its validation in low contrast ultrasound database of 885 patient Indian population epidemiological study: results of AtheroEdge™ Software. Int Angiol 31(1):42–53

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Molinari F, Pattichis CS, Zeng G et al (2012) Completely automated multiresolution edge snapper—a new technique for an accurate carotid ultrasound IMT measurement: clinical validation and benchmarking on a multi-institutional database. IEEE Trans Image Process 21(3):1211–1222

    Article  PubMed  Google Scholar 

  41. Molinari F, Meiburger KM, Saba L et al (2012) Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: our review and experience using four fully automated and one semi-automated methods. Comput Methods Programs Biomed 108(3):946–960

    Article  PubMed  Google Scholar 

  42. Molinari F, Meiburger KM, Zeng G et al (2012) Carotid artery recognition system: a comparison of three automated paradigms for ultrasound images. Med Phys 39(1):378–391

    Article  PubMed  Google Scholar 

  43. Rocha R, Campilho A, Silva J et al (2010) Segmentation of the carotid intima-media region in B-mode ultrasound images. Image Vis Comput 28(4):614–625

    Article  Google Scholar 

  44. Rocha R, Campilho A, Silva J et al (2011) Segmentation of ultrasound images of the carotid using RANSAC and cubic splines. Comput Methods Programs Biomed 101(1):94–106

    Article  PubMed  Google Scholar 

  45. Rocha R, Silva J, Campilho A (2012) Automatic segmentation of carotid B-mode images using fuzzy classification. Med Biol Eng Comput 50(5):533–545

    Article  PubMed  Google Scholar 

  46. Rocha R, Silva J, Campilho A (2014) Automatic detection of the carotid lumen axis in B-mode ultrasound images. Comput Methods Programs Biomed 115(3):110–118

    Article  PubMed  Google Scholar 

  47. Saba L, Raz E, di Martino M et al (2015) Is there an association between asymmetry of carotid artery wall thickness (ACAWT) and cerebrovascular symptoms? Int J Neurosci 125(6):456–463

    Article  PubMed  Google Scholar 

  48. Saba L, Araki T, Kumar KP et al (2016) Carotid inter-adventitial diameter is more strongly related to plaque score than lumen diameter: an automated tool for stroke analysis. J Clin Ultrasound 44(4):210–220

    Article  PubMed  Google Scholar 

  49. Santos AMF, Tavares JMRS, Sousa L et al (2013) Automatic segmentation of the lumen of the carotid artery in ultrasound B-mode images. Expert Syst Appl 40(16):6570–6579

    Article  Google Scholar 

  50. Sifakis EG, Golemati S (2014) Robust carotid artery recognition in longitudinal B-mode ultrasound images. IEEE Trans Image Process 23(9):3762–3772

    Article  PubMed  Google Scholar 

  51. Soille P (2013) Morphological image analysis: principles and applications. Springer Science & Business Media, Berlin

    Google Scholar 

  52. Suri JS, Laxminarayan S (2002) PDE and level sets. Springer Science & Business Media, Berlin

    Google Scholar 

  53. Suri JS, Haralick RM, Sheehan FH (2000) Greedy algorithm for error correction in automatically produced boundaries from low contrast ventriculograms. Pattern Anal Appl 3(1):39–60

    Article  Google Scholar 

  54. Suri JS, Liu K, Singh S et al (2002) Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review. IEEE Trans Inf Technol Biomed 6(1):8–28

    Article  PubMed  Google Scholar 

  55. Suri JS, Yuan C, Wilson DL (eds) (2005) Plaque imaging: pixel to molecular level, vol 113. IOS Press, Amsterdam

    Google Scholar 

  56. Suri JS, Kathuria C, Molinari F (2010) Atherosclerosis disease management. Springer Science & Business Media, Berlin

    Google Scholar 

  57. Suri JS, Ikeda N, Gupta A et al (2015) 2083060 A new technique for compartmental-IMT estimation in presence of bulb in carotid ultrasound scans: a stroke risk assessment system. Ultrasound Med Biol 41(4):S74

    Article  Google Scholar 

  58. WHO factsheet on cardiovascular disease. (Last updated on January 2015). http://www.who.int/mediacentre/factsheets/fs317/en/

  59. Wilhjelm JE, Gronholdt ML, Wiebe B et al (1998) Quantitative analysis of ultrasound B-mode images of carotid atherosclerotic plaque: correlation with visual classification and histological examination. IEEE Trans Med Imaging 17(6):910–922

    Article  CAS  PubMed  Google Scholar 

  60. Wu Q, Yang B, Cao C et al (2016) Age-dependent impact of inferior alveolar nerve transection on mandibular bone metabolism and the underlying mechanisms. J Mol Histol. doi:10.1007/s10735-016-9697-9

    Google Scholar 

  61. Xiao G, Brady M, Noble JA et al (2002) Segmentation of ultrasound B-mode images with intensity inhomogeneity correction. IEEE Trans Med Imaging 21(1):48–57

    Article  PubMed  Google Scholar 

  62. Zahnd G, Orkisz M, Sérusclat A et al (2014) Simultaneous extraction of carotid artery intima-media interfaces in ultrasound images: assessment of wall thickness temporal variation during the cardiac cycle. Int J Comput Assist Radiol Surg 9(4):645–658

    Article  PubMed  Google Scholar 

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Correspondence to Jasjit S. Suri.

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Dr. Jasjit S. Suri has a relationship with AtheroPoint™, Roseville, CA, USA, which is dedicated to Atherosclerosis Disease Management including Stroke and Cardiovascular imaging.

Appendix

Appendix

See Tables 5, 6, 7 and 8.

Table 5 T test for Auto LD versus manual tracings
Table 6 T test for Auto IAD versus manual tracings
Table 7 Friedman test for Auto LD versus manual tracings
Table 8 Friedman test for Auto IAD versus manual tracings

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Krishna Kumar, P., Araki, T., Rajan, J. et al. Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach. Med Biol Eng Comput 55, 1415–1434 (2017). https://doi.org/10.1007/s11517-016-1601-y

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