An efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaque using bidimensional empirical mode decomposition technique


Atherosclerosis is a type of cardiovascular disease which may cause stroke. It is due to the deposition of fatty plaque in the artery walls resulting in the reduction of elasticity gradually and hence restricting the blood flow to the heart. Hence, an early prediction of carotid plaque deposition is important, as it can save lives. This paper proposes a novel data mining framework for the assessment of atherosclerosis in its early stage using ultrasound images. In this work, we are using 1353 symptomatic and 420 asymptomatic carotid plaque ultrasound images. Our proposed method classifies the symptomatic and asymptomatic carotid plaques using bidimensional empirical mode decomposition (BEMD) and entropy features. The unbalanced data samples are compensated using adaptive synthetic sampling (ADASYN), and the developed method yielded a promising accuracy of 91.43%, sensitivity of 97.26%, and specificity of 83.22% using fourteen features. Hence, the proposed method can be used as an assisting tool during the regular screening of carotid arteries in hospitals.

Outline for our efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaques

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  1. 1.

    WHO, CVD. [Online]. Available:

  2. 2.

    Carr S, Farb A, Pearce WH, Virmani R, Yao JS (1996) Atherosclerotic plaque rupture in symptomatic carotid artery stenosis. J Vasc Surg 23(5):755–765.

    Article  PubMed  CAS  Google Scholar 

  3. 3.

    Brott TG, Hobson RW, Howard G, Roubin GS, Clark WM, Brooks W, Mackey A, Hill MD, Leimgruber PP, Sheffet AJ, Howard VJ, Moore WS, Voeks JH, Hopkins LN, Cutlip DE, Cohen DJ, Popma JJ, Ferguson RD, Cohen SN, Blackshear JL, Silver FL, Mohr JP, Lal BK, Meschia JF (2010) Stenting versus endarterectomy for treatment of carotid-artery stenosis. N Engl J Med 363(1):11–23.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. 4.

    European Carotid Surgery Trialists’ Collaborative Group (1998) Randomised trial of endarterectomy for recently symptomatic carotid stenosis: final results of the MRC European Carotid Surgery Trial (ECST). Lancet 351(9113):1379–1387

    Article  Google Scholar 

  5. 5.

    Polak JF, Shemanski L, O’Leary DH, Lefkowitz D, Price TR, Savage PJ, Brant WE, Reid C (1998) Hypoechoic plaque at US of the carotid artery: an independent risk factor for incident stroke in adults aged 65 years or older. Cardiovasc Health Study Radiol 208(3):649–654.

    CAS  Article  Google Scholar 

  6. 6.

    Inzitari D, Eliasziw M, Gates P, Sharpe BL, Chan RK, Meldrum HE, Barnett HJ (2000) The causes and risk of stroke in patients with asymptomatic internal-carotid-artery stenosis. North American Symptomatic Carotid Endarterectomy Trial Collaborators. N Engl J Med 342(23):1693–1700

    Article  PubMed  CAS  Google Scholar 

  7. 7.

    Faust O, Acharya UR, Sudarshan VK, San Tan R, Yeong CH, Molinari F, Ng KH (2017) Computer aided diagnosis of coronary artery disease, myocardial infarction and carotid atherosclerosis using ultrasound images: a review. Phys Med 33:1–15.

    Article  PubMed  Google Scholar 

  8. 8.

    UR Acharya OF, Molinari F, Saba L, Nicolaides A, Suri JS (2012) An accurate and generalized approach to plaque characterization in 346 carotid ultrasound scans. IEEE Trans Instrum Meas 61(4):1045–1053.

    Article  Google Scholar 

  9. 9.

    Szekely N, Toth N, Pataki B (2006) A hybrid system for detecting masses in mammographic images. IEEE Trans Instrum Meas 55(3):944–952.

    Article  Google Scholar 

  10. 10.

    Stoitsis J, Golemati S, Nikita KS (2006) A modular software system to assist interpretation of medical images—application to vascular ultrasound images. IEEE Trans Instrum Meas 55(6):1944–1952.

    Article  Google Scholar 

  11. 11.

    Suri JS, Kathuria C, Molinari F (2011) Atherosclerosis disease management. Springer-Verlag, New York.

    Book  Google Scholar 

  12. 12.

    Kyriacou EC, Pattichis C, Pattichis M, Loizou C, Christodoulou C, Kakkos SK, Nicolaides A (2010) A review of noninvasive ultrasound image processing methods in the analysis of carotid plaque morphology for the assessment of stroke risk. IEEE Trans Inf Technol Biomed 14(4):1027–1038.

    Article  PubMed  Google Scholar 

  13. 13.

    Christodoulou CI, Pattichis CS, Pantziaris M, Nicolaides A (2003) Texture based classification of atherosclerotic carotid plaques. IEEE Trans Med Imag 22(7):902–912.

    Article  CAS  Google Scholar 

  14. 14.

    Kyriacou E, Pattichis MS, Christodoulou CI, Pattichis CS, Kakkos S, Griffing N, Nicolaides A (2005) Ultrasound imaging in the analysis of carotid plaque morphology for the assessment of stroke. Stud Health Technol Inform 113:241–275

    PubMed  Google Scholar 

  15. 15.

    Kyriacou E, Pattichis M, Pattichis CS, Mavrommatis A, Christodoulou CI, Kakkos S, Nicolaides A (2009) Classification of atherosclerotic carotid plaques using morphological analysis on ultrasound images. J Appl Intell 30(1):3–23.

    Article  Google Scholar 

  16. 16.

    Stoitsis J, Golemati S, Nikita KS, Nicolaides AN (2004) Characterization of carotid atherosclerosis based on motion and texture features and clustering using fuzzy c-means, in Conf Proc IEEE Eng Med Biol Soc, pp 1407–1410

  17. 17.

    Acharya UR, Faust O, Alvin AP, Vinitha Sree S, Molinari F, Saba L, Nicolaides A, Suri JS (2012) Symptomatic vs. asymptomatic plaque classification in carotid ultrasound. J Med Syst 36(3):1861–1871.

    Article  PubMed  Google Scholar 

  18. 18.

    Mougiakakou SG, Golemati S, Gousias I, Nicolaides A, Nikita K (2007) Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, Laws’ texture and neural networks. Ultrasound Med Biol 33(1):26–36.

    Article  PubMed  Google Scholar 

  19. 19.

    Seabra J, Pedro LM, Fernandes FE, Sanches J (2010) Ultrasonographic characterization and identification of symptomatic carotid plaques, in Proc. 32th Annu. Int. Conf. IEEE EMBS, pp 6110–6113

  20. 20.

    Tsiaparas NN, Golemati S, Andreadis I, Stoitsis JS, Valavanis I, Nikita KS (2011) Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound. IEEE Trans Inf Technol Biomed 15(1):130–137.

    Article  PubMed  Google Scholar 

  21. 21.

    Carter-Monroe N, Yazdani SK, Ladich E, Kolodgie FD, Virmani R (2011) Introduction to the pathology of carotid atherosclerosis: histologic classification and imaging correlation, in Atherosclerosis Disease Management. Springer-Verlag, New York, pt. 1, pp 3–35

  22. 22.

    Griffin MB, Kyriacou E, Pattichis C, Bond D, Kakkos SK, Sabetai M, Geroulakos G, Georgiou N, Dore CJ, Nicolaides A (2010) Juxtaluminal hypoechoic area in ultrasonic images of carotid plaques and hemispheric symptoms. J Vasc Surg 52(1):69–76.

    Article  PubMed  Google Scholar 

  23. 23.

    Lekadir K, Galimzianova A, Betriu A, del Mar Vila M, Igual L, Rubin DL, Fernandez E, Radeva P, Napel S (2017) A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound. IEEE J Biomed Health Inform 21(1):48–55.

    Article  PubMed  Google Scholar 

  24. 24.

    Hamid H, Najmeh S, Salehi SMM (2015) Using morphological transforms to enhance the contrast of medical images. Egypt J Radiol Nucl Med 46(2):481–489

    Article  Google Scholar 

  25. 25.

    Keys R (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160.

    Article  Google Scholar 

  26. 26.

    Pizer SM, Amburn EP, Austin JD, Cromarrtie R, Geselowitz A, Greer T, Romeny H, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vision, Graph, Image Process 39(3):355–368.

    Article  Google Scholar 

  27. 27.

    Wang X, Wong BS, Guan TC (2004) Image enhancement for radiography inspection, Proc. SPIE 5852, Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics, vol. 462

  28. 28.

    Nunes JC, Bouaoune Y, Delechelle E, Niang O, Bunel P (2003) Image analysis by bidimensional empirical mode decomposition. Image Vis Comput 21(12):1019–1026.

    Article  Google Scholar 

  29. 29.

    Nunes JC, Guyot S, Deléchelle E (2005) Texture analysis based on local analysis of the bidimensional empirical mode decomposition. Mach Vision Appl 16(3):177–188

    Article  Google Scholar 

  30. 30.

    Shao Y, Celenk M (2001) Higher-order spectra (HOS) invariants for shape recognition. Pattern Recogn 34(11):2097–2113.

    Article  Google Scholar 

  31. 31.

    Acharya UR, Dua S, Du X, Vinitha Sree S, Chua CK (2011) Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans Inf Technol Biomed 15(3)

  32. 32.

    Shannon CE (1948) A mathematical theory of communication. Bell Syst Technol J 27(3):379–423.

    Article  Google Scholar 

  33. 33.

    Renyi A (1961) On measures of entropy and information. In: Proceedings of the fourth Berkeley symposium on mathematical statistics and probability, vol. 1, pp 547–561

  34. 34.

    Chen WT, Wang ZZ, Xie HB, Yu WX (2007) Characterization of surface EMG signal based on fuzzy entropy. IEEE Trans Neural Syst and Rehabil Eng 15(2):266–272.

    Article  Google Scholar 

  35. 35.

    Kapur JN (1968) Information of order α and type β. Proc Ind Acad Sci 68:65–75

    Google Scholar 

  36. 36.

    Ghosh M, Chakraborty C, Ray AK (2013) Yager's measure based fuzzy divergence for microscopic color image segmentation, in Indian Conference on Medical Informatics and Telemedicine, Kharagpur, pp 13–16

  37. 37.

    Yin P-Y (2002) Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization. Signal Process 82(7):993–1006.

    Article  Google Scholar 

  38. 38.

    He H, Yang B, Garcia EA, Li S ADASYN: adaptive synthetic sampling approach for imbalanced learning, Proceedings of the International Joint Conference on Neural Networks,{IJCNN} 2008, part of the IEEE World Congress on Computational Intelligence,{WCCI} 2008, Hong Kong, China, pp 1–6

  39. 39.

    He X, Cai D, Yan S, Zhang H-J (2005) Neighborhood preserving embedding, Proceedings of the Tenth IEEE International Conference on Computer Vision—vol. 2, pp 1208—1213

  40. 40.

    Gudigar A, Chokkadi S, Raghavendra U, Acharya UR (2017) Multiple thresholding and subspace based approach for detection and recognition of traffic sign. Multimed Tools Appl 76(5):6973–6991.

    Article  Google Scholar 

  41. 41.

    Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37.

    Article  Google Scholar 

  42. 42.

    Kailath T (1967) The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans Commun Technol 15(1):52–60.

    Article  Google Scholar 

  43. 43.

    Student t-test, Last Accessed: 26.02.2017. [Online]. Available:

  44. 44.

    Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83.

    Article  Google Scholar 

  45. 45.

    Sundar N, Lipsitz SR, Fitzmaurice GM, Sinha D, Ibrahim JG, Haas J, Gellad W (2012) An extension of the Wilcoxon rank-sum test for complex sample survey data. J R Stat Soc: Ser C: Appl Stat 61(4):653–664

    Article  Google Scholar 

  46. 46.

    Obuchowski NA (2003) Receiver operating characteristic curves and their use in radiology. Radiology 229(1):3–8.

    Article  PubMed  Google Scholar 

  47. 47.

    Dash M, Liu H (1999) Handling large unsupervised data via dimensionality reduction, In ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery

  48. 48.

    Abe N, Kudo M (2005) Entropy criterion for classifier-independent feature selection, in knowledge-based intelligent information and engineering systems, ser. Lecture notes in computer science. Springer Berlin Heidelberg, vol. 3684, pp 689–695

  49. 49.

    Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans Electron Comput 14(3):326–334

    Article  Google Scholar 

  50. 50.

    Larose DT (2004) Discovering knowledge in data: an introduction to data mining. Wiley-Interscience, Hoboken.

    Book  Google Scholar 

  51. 51.

    Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118.

    Article  Google Scholar 

  52. 52.

    Kecman DV (2001) Learning and soft computing. MIT Press, Cambridge

    Google Scholar 

  53. 53.

    Acharya UR, Fujita H, Bhat S, Raghavendra U, Gudigar A, Molinari F, Vijayananthan A, Ng KH (2016) Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images. Inf Fusion 29:32–39.

    Article  Google Scholar 

  54. 54.

    Raghavendra U, Acharya UR, Gudigar A, Shetty R, Krishnananda N, Pai U, Samanth J, Nayak C (2017) Automated screening of congestive heart failure using variational mode decomposition and texture features extracted from ultrasound images, Neural Computing and Applications, Springer.

  55. 55.

    Seabra J, Ciompi F, Pujol O, Mauri J, Radeva P, Sanchez J (2011) Rayleigh mixture model for plaque characterization in intravascular ultrasound. IEEE Trans Biomed Eng 58(5):1314–1324.

    Article  PubMed  Google Scholar 

  56. 56.

    Tsiaparas NN, Golemati S, Andreadis I, Stoitsis J, Valavanis I, Nikita KS (2012) Assessment of carotid atherosclerosis from B-mode ultrasound images using directional multiscale texture features. Meas Sci Technol 23(11):114004.

    Article  CAS  Google Scholar 

  57. 57.

    Acharya UR, Vinitha Sree S, Mookiah MRK, Molinari F, Saba L, Yee S, Ho S, Ahuja AT, Ho SC, Nicolaides A, Suri JS (2012) Atherosclerotic risk stratification strategy for carotid arteries using texture-based features. Ultrasound Med Biol 38(6):899–915.

    Article  PubMed  Google Scholar 

  58. 58.

    Acharya UR, Mookiah MRK, Vinitha Sree S, Sanches J, Shafique S, Nicolaides A, Pedro LM, Suri JS (2013) Plaque tissue characterization and classification in ultrasound carotid scans: a paradigm for vascular feature amalgamation. IEEE Trans Instrum Meas 62(2):392–400.

    Article  Google Scholar 

  59. 59.

    Acharya UR, Mookiah MRK, Vinitha Sree S, Afonso D, Sanches J, Shafique S, Nicolaides A, Pedro LM, Fernandes e Fernandes J, Suri JS (2013) Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment. Med Biol Eng Comput 51(5):513–523.

    Article  PubMed  Google Scholar 

  60. 60.

    Acharya UR, Faust O, Alvin APC, Krishnamurthi G, Seabra JCR, Sanches J, Suri JS (2013) Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization. Comput Methods Prog Biomed 110(1):66–75.

    Article  Google Scholar 

  61. 61.

    Afonso D, Seabra J, Pedro LM, Fernandes JF, Sanches JM (2015) An ultrasonographic risk score for detecting symptomatic carotid atherosclerotic plaques. IEEE J Biomed Health Inf 19(4):1505–1513.

    Article  Google Scholar 

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Corresponding author

Correspondence to Filippo Molinari.

Ethics declarations

All the images were acquired after the subjects signed an informed consent about the treatment of their data. The use of the images was approved by the institutional review board of the Gradenigo Hospital.



Table 10 Results of neighborhood preserving embedding features obtained using t test ranking method

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Molinari, F., Raghavendra, U., Gudigar, A. et al. An efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaque using bidimensional empirical mode decomposition technique. Med Biol Eng Comput 56, 1579–1593 (2018).

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  • Atherosclerosis
  • Carotid plaque
  • Neighborhood preserving
  • BEMD
  • SVM