Journal of Medical Systems

, 42:154 | Cite as

Convolutional Neural Network for Segmentation and Measurement of Intima Media Thickness

  • Sudha S.
  • Jayanthi K. B.
  • Rajasekaran C.
  • Nirmala Madian
  • Sunder T.
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing


The measurement of Carotid Intima Media Thickness (IMT) on Common Carotid Artery (CCA) is a principle marker of risk of cardiovascular disease. This paper presents a novel method of using deep Convolutional Neural Network (CNN) for identification and measurement of IMT on the far wall of the artery. The Region of Interest (ROI) is extracted using CNN architecture with 8 layers. 110 subjects are taken for the study. Each subject is recorded with one Right Common Carotid Artery (RCCA) and Left Common Carotid Artery (LCCA) frame resulting in 220 recordings. Patch based segmentation with 2640 patches are given to the training network for ROI localization. Intima Media Complex (IMC) is the area where IMT is measured. This region is extracted after defining the ROI. Keeping in mind the end objective of measurement of IMT values binary threshold with snake algorithm is applied to extract the lumen-intima and media-adventitia boundary. IMT values are measured for 20 cases and mean difference is found to be 0.08 mm.


Carotid intima media thickness (CIMT) Deep learning Cardio vascular disease (CVD) Convolutional neural network (CNN) 



The project is supported by Department of Biotechnology, New Delhi, India (Ref. No: BT/PR16298/BID/7/581/2016). The college is supported under DST FIST, India.

Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest in submitting the paper to Journal of Medical systems.


  1. 1.
    Balasundaram, J. K., and Wahida Banu, R. S. D., A non-invasive study of alteration of the carotid artery with age using ultrasound images. Med. Biol. Eng. Comput. 44(9):767–772, 2006.Google Scholar
  2. 2.
    Pignoli, P., and Longo, T., Evaluation of atherosclerosis with b-mode ultrasound imaging. J. Nucl. Med. Allied Sci. 32:166–173, 1988.Google Scholar
  3. 3.
    Liang, Q., Wendelhag, I., Wikstrand, J., and Gustavsson, T., A multiscale dynamic programming procedure for boundary detection in ultrasonic artery images. Med. Imaging, IEEE Trans. 19(2):127–142, 2000.Google Scholar
  4. 4.
    Selzer, R. H., Mack, W. J., Lee, P. L., Kwong-Fu, H., and Hodis, H. N., Improved common carotid elasticity and intima-media thickness mea-surements from computer analysis of sequential ultrasound frames. Atherosclerosis. 154(1):185–193, 2001.Google Scholar
  5. 5.
    Cheng, D., Schmidt, A., Cheng, K., and Burkhardt, H., Using snakes to detect the intimal and adventitial layers of the common carotid artery wall in sonographic images. Comput. Methods Prog. Biomed. 67(1):27–37, 2002.Google Scholar
  6. 6.
    Stein, J. H., Korcarz, C. E., Mays, M. E., Douglas, P. S., Palta, M., Zhang, H., LeCaire, T., Paine, D., Gustafson, D., and Fan, L., A semiautomated ultrasound border detection program that facilitates clinical measurement of ultrasound carotid intima-media thickness. J. Am. Soc. Echocardiogr. 18(3):244–251, 2005.Google Scholar
  7. 7.
    Faita, F., Gemignani, V., Bianchini, E., Giannarelli, C., Ghiadoni, L., and Demi, M., Real- time measurement system for evaluation of the carotid intima-media thickness with a robust edge operator. J. Ultrasound Med. 27(9):1353–1361, 2008.Google Scholar
  8. 8.
    Liguori, C., Paolillo, A., and Pietrosanto, A., An automatic measurement system for the evaluation of carotid intima-media thickness. IEEE Trans. Instrum. Meas. 50(6):1684–1691, 2001.Google Scholar
  9. 9.
    Selzer, R. H., Mack, W. J., Lee, P. L., Kwong-Fu, H., and Hodis, H. N., Improved common carotid elasticity and intima-media thickness measurements from computer analysis of sequential ultrasound frames. Atherosclerosis. 154(1):185–193, 2001.Google Scholar
  10. 10.
    Wendelhag, I., Liang, Q., Gustavsson, T., and Wikstrand, J., A new automated computerized analysis system simplifies readings and reduces the variability in ultrasound measurement of intima-media thickness. Stroke. 28:2195–2200, 1997.Google Scholar
  11. 11.
    Gustavsson, T., Liang, Q., Wendelhag, I., and Wikstrand, J., A dynamic programming procedure for automated ultrasonic measurement of the carotid artery. Comput. Cardiol. :297–300, 1994.Google Scholar
  12. 12.
    Cheng, D.-C., and Jiang, X., Detections of arterial wall in sonographic artery images using dual dynamic programming. IEEE Trans. Inf. Technol. Biomed. 12(6):792–799, 2008.Google Scholar
  13. 13.
    Santhiyakumari, N., and Madheswaran, M., Non-invasive evaluation of carotid artery wall thickness using improved dynamic programming technique. SIViP. 2(2):183–193, 2008.Google Scholar
  14. 14.
    Lee, Y.-B., Choi, Y.-J., and Kim, M.-H., Boundary detection in carotid ultrasound images using dynamic programming and a directional haar-like filter. Comput. Biol. Med. 40(8):687–697, 2010.Google Scholar
  15. 15.
    Delsanto, S., Molinari, F., Giusetto, P., Liboni, W., Badalamenti, S., and Suri, J., Characterization of a completely user-independent algorithm for carotid artery segmentation in 2-d ultrasound images. IEEE Trans. Instrum. Meas. 56(4):1265–1274, 2007.Google Scholar
  16. 16.
    Chan, R., Kaufhold, J., Hemphill, L., Lees, R., and Karl, W., Anisotropic edge-preserving smoothing in carotid b-mode ultrasound for improved segmentation and intima-media thickness (imt) measurement. Comput Cardiol. :37–40, 2000.Google Scholar
  17. 17.
    Gutierrez, M. A., Pilon, P. E., Lage, S. G., Kopel, L., Carvalho, R. T., and Furuie, S. S., Automatic measurement of carotid diameter and wall thickness in ultrasound images. IEEE Comput. Cardiol. :359–362, 2002.Google Scholar
  18. 18.
    Loizou, C. P., Pattichis, C. S., Pantziaris, M., Tyllis, T., and Nicolaides, A., Snakes based segmentation of the common carotid artery intima media. Med. Biol. Eng. Comput. 45:35–49, 2007.
  19. 19.
    Ceccarelli, M., De Luca, N., and Morganella, A., An active contour approach to automatic detection of the intima-media thickness. Proc. IEEE Int. Conf. Acoust, Speech Signal Process. ICASSP. 2:709–712, 2006.Google Scholar
  20. 20.
    Molinari, F., Meiburger, K. M., Saba, L., Acharya, U. R., Ledda, M., Nicolaides, A. N., and Suri, J. S., Constrained snake vs. conventional snake for carotid ultrasound automated IMT measurements on multi-center data sets. Ultrasonics. 52(7):949–961, 2012.Google Scholar
  21. 21.
    Petroudi, S., Loizou, C., Pantziaris, M., and Pattichis, C., Segmentation of the common carotid intima-media complex in ultrasound images using active contours. IEEE Trans. Biomed. Eng. 59(11):3060–3069, 2012.Google Scholar
  22. 22.
    Santos, A. M. F., Tavares, J. M. R. S., Sousa, L., dos Santos, R. M., Castro, P., and Azevedo, E., Automatic segmentation of the lumen of the carotid artery in ultrasound B-mode images. Med. Imaging: Computer-Aided Diagn. 2013.Google Scholar
  23. 23.
    Nagaraj, Y., et al., Segmentation of Intima Media Complex from Carotid Ultrasound Images using Wind Driven Optimization Technique. Biomed. Signal Process. Control. 2017.
  24. 24.
    Bastida-Jumilla, M. C., Menchón-Lara, R. M., Morales-Sánchez, J., Verdú-Monedero, R., Larrey-Ruiz, J., and Sancho-Gómez, J. L., Frequency-domain active contours solution to evaluate intima–media thickness of the common carotid artery. Biomed. Signal Process. Control. 16:68–79, 2015.Google Scholar
  25. 25.
    Gutierrez, M. A., Pilon, P.E., Lage S.G., Kopel L., Carvalho R. T., Furuie, S.S., Automatic Measurement of Carotid Diameter and Wall Thickness in Ultrasound Images. IEEE Comput. Cardiol. 29:359–362, 2002.Google Scholar
  26. 26.
    Delsanto, S., Molinari, F., Liboni, W. et al., User-independent plaque characterization and accurate IMT measurement of carotid artery wall using ultrasound. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1:2404–7, 2006.Google Scholar
  27. 27.
    Menchón-Lara, R.-M., and Sancho-Gómez J.-L., Fully automatic segmentation of ultrasound common carotid artery images based on machine learning. Neurocomputing. 151(Part 1): 161–167, 2015.Google Scholar
  28. 28.
    Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and Lecun, Y., Overfeat: Integrated recognition, localization and detection using convolutional networks. In: International Conference on Learning Representations (ICLR2014). CBLS, 2014.,
  29. 29.
    Litjens, G. J. S., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J., van Ginneken, B., and Sánchez, C. I., A survey on deep learning in medical image analysis. Med. Image Anal. 42:60–88, 2017.Google Scholar
  30. 30.
    Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Pérez, J. A., Lo, B. P. L., and Yang, G.-Z., Deep Learning for Health Informatics. IEEE J. Biomed. Health Informatics. 21(1):4–21, 2017.Google Scholar
  31. 31.
    Tharani, S., and Yamini, C., Classification using Convolutional Neural Network for Heart and Diabetics Datasets. Int. J. Adv. Res. Comput. Commun. Eng. 5(12):417–422, 2016.Google Scholar
  32. 32.
    Le Q. V., A Tutorial on Deep LearningPart 1: Nonlinear Classifiers and The Backpropagation Algorithm. 2015.
  33. 33.
    Anuse, A. and Vyas V., A novel training algorithm for convolutional neural network. Complex Intell Syst. 2(3):221–234, 2016.Google Scholar
  34. 34.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R., Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1):1929–1958, 2014.Google Scholar
  35. 35.
    Baldi, P., and Sadowski, P., The dropout learning algorithm. Artif Intell. 210:78–122, 2014.
  36. 36.
    Greenspan, H., Ginneken, B. V., and Summers, R. M., Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans. Med. Imag. 35:1153–1159, 2016.Google Scholar
  37. 37.
    Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., and Greenspan H., Fully Convolutional Network for Liver Segmentation and Lesions Detection. In: Carneiro, G. et al. (Eds.), Deep Learning and Data Labeling for Medical Applications. DLMIA 2016, LABELS 2016. Lecture Notes in Computer Science. Vol. 10008. Springer, Cham, 2016.Google Scholar
  38. 38.
    Krizhevsky, A., Sutskever, I., and Hinton, G. E., ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, pp. 1097–1105. Lake Tahoe, Nevada, 2012.Google Scholar
  39. 39.
    Hiremath, P. S., Akkasaligar, P. T., and Badiger, S., Speckle Noise Reduction in Medical Ultrasound Images. Advancements Breakthroughs Ultrasound Imaging, 2013.
  40. 40.
    Xiao, L., Li, Q., Bai, Y., Zhang, L., and Tang, J., Automated Measurement Method of Common Carotid Artery Intima-Media Thickness in Ultrasound Image Based on Markov Random Field Models. J. Med. Biol. Eng. 35(5):651–660, 2015.Google Scholar
  41. 41.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I, and Salakhutdinov, R., Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1):1929–1958, 2014Google Scholar
  42. 42.
    Bots, M. L., Hoes, A. W., Koudstaal, P. J., Hofman, A., and Grobbee, D. E., Common carotid intima-media thickness and risk of stroke and myocardial infarction: the Rotterdam Study. US Natl. Libr. Med. Natl. Inst. Health. 96(5):1432–1437, 1997.Google Scholar
  43. 43.
    Aswathy, M. A., Santha, S., and Jayanthi, K. B., Analysis of the performance of various algorithms for the segmentation of the carotid artery. IEEE Point-of-Care Healthcare Technologies (PHT), 2013.Google Scholar
  44. 44.
    Ramasamy, N., and Jayanthi, K. B., Automated lumen segmentation and estimation of numerical attributes of common carotid artery using longitudinal B-mode ultrasound images. IEEE Point-of-Care Healthcare Technologies (PHT), 2013.Google Scholar
  45. 45.
    Jayanthi, K. B., Rajasekaran, C., Madian, N., and Thyagarajah, K., Analysis on various segmentation techniques – IMT measurement of common carotid artery. TENCON 2017 - 2017 IEEE Region 10 Conference.Google Scholar
  46. 46.
    Barbu, A. et al., An analysis of robust cost functions for CNN in computer-aided diagnosis. CMBBE: Imaging Vis. 6:253–258, 2018.Google Scholar
  47. 47.
    Kingma, D., Ba, J., Adam: A Method for Stochastic Optimization, 3rd International Conference for Learning Representations, San Diego, pp. 1–13, 2015.Google Scholar
  48. 48.
    Luo, Y., Liu, L., Huang, Q., and Li, X., A novel segmentation approach combining region-and edge-based information for ultrasound images. Biomed. Res. Int. 2017, 2017.Google Scholar
  49. 49.
    Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., and Barkan, E., A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography. In: Carneiro, G. et al. (Eds.), Deep Learning and Data Labeling for Medical Applications. DLMIA 2016, LABELS 2016. Lecture Notes in Computer Science. Vol. 10008. Cham: Springer, 2016.Google Scholar
  50. 50.
    Chan, T. F., Vese, L. A., Active contours without edges. IEEE Trans. Image Process. 10(2):266–277, 2001.Google Scholar
  51. 51.
    Baswaraj, D., Govardhan, A., Premchand, P., Active Contours and Image Segmentation: The Current State of the Art. Global J. Comp. Sci. Technol. Graph. Vis. 12(11):Version 1.0, 2012.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sudha S.
    • 1
  • Jayanthi K. B.
    • 1
  • Rajasekaran C.
    • 1
  • Nirmala Madian
    • 2
  • Sunder T.
    • 3
  1. 1.Department of Electronics and Communication EngineeringK.S.Rangasamy College of TechnologyTamil NaduIndia
  2. 2.Department of Electronics and Communication EngineeringSri Shakthi Institute of Engineering and TechnologyCoimbatoreIndia
  3. 3.Apollo HospitalsChennaiIndia

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