Skip to main content
Springer Nature Link
Log in

Completely automated robust edge snapper for carotid ultrasound IMT measurement on a multi-institutional database of 300 images

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

The carotid intima-media thickness (IMT) is the most used marker for the progression of atherosclerosis and onset of cardiovascular diseases. Computer-aided measurements improve accuracy and precision, but usually require user interaction. In this paper we characterized a new and completely automated technique for carotid segmentation and IMT measurement based on the merits of two previously developed techniques. We used an integrated approach of intelligent image feature extraction and line fitting for automatically locating the carotid artery in the image frame, followed by wall interfaces extraction based on a Gaussian edge operator. We called our system—CARES. We validated CARES on a multi-institutional database of 300 carotid ultrasound images. The IMT measurement bias was 0.032 ± 0.141 mm. Our novel approach of CARES processed 96% of the images in the database taken from two different institutions. In order to evaluate its performance, the figure-of-merit (FoM) was defined as the percent ratio between the average IMT computed by CARES and the one obtained from manual tracings by expert sonographers. The estimated FoM by CARES was 95.7%. Comparing the IMT bias of CARES with our previously published method CALEX that showed an IMT bias equal to 0.099 ± 0.137 mm, CARES improved the IMT accuracy by 67%, while increasing the standard deviation by 3%. CARES could be a useful research tool for processing large datasets in multi-center studies involving atherosclerosis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

CVD:

Cardiovascular disease

IMT:

Intima-media thickness

CA:

Carotid artery

CALEX:

Completely automated layers extraction technique

FOAM:

First-order absolute moment

CARES:

Completely automated robust edge snapper

LI:

Lumen-intima

MA:

Media-adventitia

ROI:

Region of interest

ADF :

Far (distal) adventitia layer

ADN :

Near (proximal) adventitia layer

PDM:

Polyline distance measure

GT:

Ground-truth

CALEXLI :

LI tracing by CALEX

CALEXMA :

MA tracing by CALEX

CARESLI :

LI tracing by CARES

CARESMA :

MA tracing by CARES

GTLI :

LI manual tracing (ground-truth)

GTMA :

MA manual tracing (ground-truth)

CALEXIMT :

IMT measurement by CALEX

CARESIMT :

IMT measurement by CARES

GTIMT :

IMT manual measurement (ground-truth)

\( \bar{\varepsilon }_{\text{LI}} \) :

Mean LI segmentation system error

\( \bar{\varepsilon }_{\text{MA}} \) :

Mean MA segmentation system error

\( \bar{\mu }_{\text{CALEX}} \) :

Mean CALEX IMT measurement error

\( \bar{\mu }_{\text{CARES}} \) :

Mean CARES IMT measurement error

References

  1. Alberola-Lopez C, Martin-Fernandez M, Ruiz-Alzola J (2004) Comments on: a methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans Med Imaging 23(5):658–660

    Article  PubMed  Google Scholar 

  2. Badimon JJ, Ibanez B, Cimmino G (2009) Genesis and dynamics of atherosclerotic lesions: implications for early detection. Cerebrovasc Dis 27(Suppl 1):38–47

    Article  PubMed  Google Scholar 

  3. Chalana V, Kim Y (1997) A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans Med Imaging 16(5):642–652

    Article  PubMed  CAS  Google Scholar 

  4. Cheng DC, Schmidt-Trucksass A, Cheng KS, Burkhardt H (2002) Using snakes to detect the intimal and adventitial layers of the common carotid artery wall in sonographic images. Comput Methods Programs Biomed 67(1):27–37

    Article  PubMed  Google Scholar 

  5. de Groot E, van Leuven SI, Duivenvoorden R et al (2008) Measurement of carotid intima-media thickness to assess progression and regression of atherosclerosis. Nat Clin Pract Cardiovasc Med 5(5):280–288

    Article  PubMed  Google Scholar 

  6. 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 

  7. Demi M, Paterni M, Benassi A (2000) The first absolute central moment in low-level image processing. Comput Vis Image Underst 80(1):57–87

    Article  Google Scholar 

  8. Destrempes F, Meunier J, Giroux MF, 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):215–229

    Article  PubMed  Google Scholar 

  9. Faita F, Gemignani V, Bianchini E et al (2008) Real-time measurement system for evaluation of the carotid intima-media thickness with a robust edge operator. J Ultrasound Med 27(9):1353–1361

    PubMed  Google Scholar 

  10. Fan L, Santago P, Riley W, Herrington DM (2001) An adaptive template-matching method and its application to the boundary detection of brachial artery ultrasound scans. Ultrasound Med Biol 27(3):399–408

    Article  PubMed  CAS  Google Scholar 

  11. Golemati S, Stoitsis J, Balkizas T, Nikita K (2005) Comparison of b-mode, m-mode and hough transform methods for measurement of arterial diastolic and systolic diameters. Conf Proc IEEE Eng Med Biol Soc 2(1):1758–1761

    PubMed  CAS  Google Scholar 

  12. 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):1918–1932

    Article  PubMed  Google Scholar 

  13. Jin D, Wang Y (2007) Doppler ultrasound wall removal based on the spatial correlation of wavelet coefficients. Med Biol Eng Comput 45(11):1105–1111

    Article  PubMed  Google Scholar 

  14. Kampoli AM, Tousoulis D, Antoniades C, Siasos G, Stefanadis C (2009) Biomarkers of premature atherosclerosis. Trends Mol Med 15(7):323–332

    Article  PubMed  CAS  Google Scholar 

  15. 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):127–142

    Article  PubMed  CAS  Google Scholar 

  16. Liguori C, Paolillo A, Pietrosanto A (2001) An automatic measurement system for the evaluation of carotid intima-media thickness. IEEE Trans Instrum Meas 50(6):1684–1691

    Article  Google Scholar 

  17. 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 

  18. Loizou CP, Pattichis CS, Pantziaris M, Tyllis T, Nicolaides A (2006) Quality evaluation of ultrasound imaging in the carotid artery based on normalization and speckle reduction filtering. Med Biol Eng Comput 44(5):414–426

    Article  PubMed  CAS  Google Scholar 

  19. 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):35–49

    Article  PubMed  CAS  Google Scholar 

  20. Molinari F, Delsanto S, Giustetto P et al (2008) User-independent plaque segmentation and accurate intima-media thickness measurement of carotid artery wall using ultrasound. In: Suri JS, Kathuria C, Chang RF, Molinari F, Fenster A (eds) Advances in diagnostic and therapeutic ultrasound imaging. Artech House, Norwood, MA, pp 111–140

    Google Scholar 

  21. 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 9(4):481–505

    Article  Google Scholar 

  22. Molinari F, Zeng G, Suri J (2010) Greedy technique and its validation for fusion of two segmentation paradigms leads to an accurate intima-media thickness measure in plaque carotid arterial ultrasound. J Vasc Ultrasound 34(2):63–73

    Google Scholar 

  23. 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 100:201–221

    Article  PubMed  Google Scholar 

  24. 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: a new technique. J Ultrasound Med 29:399–418

    PubMed  Google Scholar 

  25. Molinari F, Zeng G and Suri JS (2010) Effect of learning algorithm on automated tracigns of adventitia borders in atherosclerotic common carotid artery (cca) ultrasound. 2010 AIUM Annual Convention, San Diego, CA, USA

  26. Molinari F, Zeng G, Suri JS (2010) Intima-media thickness: setting a standard for completely automated method for ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control 57(5):1112–1124

    Article  PubMed  Google Scholar 

  27. Pignoli P, Longo T (1988) Evaluation of atherosclerosis with b-mode ultrasound imaging. J Nucl Med Allied Sci 32(3):166–173

    PubMed  CAS  Google Scholar 

  28. Polat K, Latifoglu F, Kara S, Gunes S (2008) Usage of a novel, similarity-based weighting method to diagnose atherosclerosis from carotid artery Doppler signals. Med Biol Eng Comput 46(4):353–362

    Article  PubMed  Google Scholar 

  29. Rossi AC, Brands PJ, Hoeks AP (2008) Automatic recognition of the common carotid artery in longitudinal ultrasound b-mode scans. Med Image Anal 12(6):653–665

    Article  PubMed  Google Scholar 

  30. Rossi AC, Brands PJ, Hoeks AP (2010) Automatic localization of intimal and adventitial carotid artery layers with noninvasive ultrasound: a novel algorithm providing scan quality control. Ultrasound Med Biol 36(3):467–479

    Article  PubMed  Google Scholar 

  31. Simon A, Gariepy J, Chironi G, Megnien JL, Levenson J (2002) Intima-media thickness: a new tool for diagnosis and treatment of cardiovascular risk. J Hypertens 20(2):159–169

    Article  PubMed  CAS  Google Scholar 

  32. Stein JH, Korcarz CE, Mays ME et al (2005) A semiautomated ultrasound border detection program that facilitates clinical measurement of ultrasound carotid intima-media thickness. J Am Soc Echocardiogr 18(3):244–251

    Article  PubMed  Google Scholar 

  33. 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 

  34. Touboul PJ, Hennerici MG, Meairs S et al (2004) Mannheim intima-media thickness consensus. Cerebrovasc Dis 18(4):346–349

    Article  PubMed  Google Scholar 

  35. Touboul PJ, Prati P, Scarabin PY et al (1992) Use of monitoring software to improve the measurement of carotid wall thickness by b-mode imaging. J Hypertens Suppl 10(5):S37–S41

    Article  PubMed  CAS  Google Scholar 

  36. van der Meer IM, Bots ML, Hofman A et al (2004) Predictive value of noninvasive measures of atherosclerosis for incident myocardial infarction: the Rotterdam study. Circulation 109(9):1089–1094

    Article  PubMed  Google Scholar 

  37. Walter M (2009) Interrelationships among hdl metabolism, aging, and atherosclerosis. Arterioscler Thromb Vasc Biol 29(9):1244–1250

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

The authors gratefully thank Dr. Marios Pantziaris (Cyprus Institute of Neurology, Nicosia, Cyprus) and Dr. William Liboni (Neurology Dept., Gradenigo Hospital, Torino, Italy) for providing the images.

Author information

Authors and Affiliations

  1. Biolab, Dipartimento di Elettronica, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Turin, Italy

    Filippo Molinari & Kristen M. Meiburger

  2. Department of ECE, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  3. MBF Bioscience, Williston, VT, USA

    Guang Zeng

  4. CTO, Global Biomedical Technologies Inc, Roseville, CA, USA

    Jasjit S. Suri

  5. Idaho State University (Aff.), Pocatello, ID, USA

    Jasjit S. Suri

Authors
  1. Filippo Molinari
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. U. Rajendra Acharya
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Guang Zeng
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Kristen M. Meiburger
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Jasjit S. Suri
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Filippo Molinari.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Molinari, F., Rajendra Acharya, U., Zeng, G. et al. Completely automated robust edge snapper for carotid ultrasound IMT measurement on a multi-institutional database of 300 images. Med Biol Eng Comput 49, 935–945 (2011). https://doi.org/10.1007/s11517-011-0781-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-011-0781-8

Keywords