Ultrasound in Medicine and Biology
Volume 33, Issue 1 , Pages 26-36 , January 2007

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, Laws’ texture and neural networks

  • Stavroula Gr. Mougiakakou

      Affiliations

    • Biomedical Simulations and Imaging Laboratory, Faculty of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
    • ,
  • Spyretta Golemati

      Affiliations

    • Biomedical Simulations and Imaging Laboratory, Faculty of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
    • ,
  • Ioannis Gousias

      Affiliations

    • Biomedical Simulations and Imaging Laboratory, Faculty of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
    • ,
  • Andrew N. Nicolaides

      Affiliations

    • Irvine Laboratory, St Mary’s Hospital, Imperial College of Science, Technology and Medicine, London, UK
    • ,
  • Konstantina S. Nikita

      Affiliations

    • Biomedical Simulations and Imaging Laboratory, Faculty of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
    • Corresponding Author InformationAddress correspondence to: Professor Konstantina S. Nikita, Biomedical Simulations and Imaging Laboratory, Faculty of Electrical and Computer Engineering, National Technical University of Athens, 9, Heroon Polytechneiou Str., 15780 Zographou, Greece.

Received 27 July 2004 ,Revised 17 July 2006 ,Accepted 27 July 2006.

References 

  1. Agostino-Accardo P, Pensiero S. Neural network-based system for early keratoconus detection from corneal topography. J Biomedical Informatics. 2003;35:151–159
  2. Arifovic J, Gençay R. Using genetic algorithms to select architecture of a feed forward artificial neural network. Physica A. 2001;289:574–594
  3. Asvestas P, Golemati S, Matsopoulos GK, Nikita KS, Nicolaides AN. Fractal dimension estimation of carotid atherosclerotic plaques from B-mode ultrasound: A pilot study. Ultrasound Med Biol. 2002;28:1129–1136
  4. Biasi GM, Sampaolo A, Mingazzini P, De Amicis P, El-Barghouty N, Nicolaides AN. Computer analysis of ultrasonic plaque echolucency in identifying high risk carotid bifurcation lesions. Eur J Vasc Endovasc Surg. 1999;17:476–479
  5. Castillo PA, Merelo JJ, Prieto A, Rivas V, Romero G. G-Prop: Global optimization of multilayer perceptrons using GAs. Neurocomputing. 2000;35(1–4):149–163
  6. Chan KL. Two approaches to motion analysis of the ultrasound image sequence of carotid atheromatous plaque. Ultrasonics. 1993;31:117–123
  7. Chen D-R, Chang R-F, Kuo W-J, Chen M-C, Huang Y-L. Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound in Med Biol. 2002;28(10):1301–1310
  8. Christodoulou CI, Pattichis CS, Pantziaris M, Nicolaides A. Texture-based classification of atherosclerotic carotid plaques. IEEE Trans Med Imaging. 2003;22:902–912
  9. Consensus group. Consensus statement on the management of patients with asymptomatic atherosclerotic carotid bifurcation lesions. Int Angiol. 1995;14:5–17
  10. Elatrozy T, Nicolaides A, Tegos T, Griffin M. The objective characterisation of ultrasonic carotid plaque features. Eur J Vasc Endovasc Surg. 1998;16:223–230
  11. European Carotid Surgery Trialists’ Collaborative Group. Randomised trial of endarterectomy for recently symptomatic carotid stenosis: Final results of the MRC European Carotid Surgery Trial (ECST). Lancet. 1998;351:1379–1387
  12. Fawcett T. ROC graphs: Notes and practical considerations for data mining researchers. Tech Report HPL-2003–2004, HP Laboratories. 2003;
  13. Foley D. Considerations of sample and feature size. IEEE Trans on Information Theory. 1972;18(5):618–626
  14. Fukunaga K, Hayes RR. Effects of sample size in classifier design. IEEE Trans Pattern Anal Machine Intell. 1989;11(8):873–885
  15. Furlong N, Lovelace E, Lovelace K. Research methods and statistics: An integrated approach. Orlando, Florida: Harcourt Brace and Company; 2000;
  16. Gletsos M, Mougiakakou S, Matsopoulos G, Nikita K, Nikita A, Kelekis D. A computer-aided diagnostic system to characterize CT focal liver lesions: Design and optimisation of a neural network classifier. IEEE Trans Inform Technol Biomed. 2003;7(3):153–162
  17. Goldberg D. Genetic algorithms in search, optimization and machine learning. Boston, MA, USA: Addison-Wesley Publishing Company, Inc; 1989;
  18. Gonzalez RC, Woods RE. Digital image processing. Boston, MA, USA: Addison-Wesley Publishing Company, Inc; 1993;
  19. Haykin S. Neural networks: A comprehensive foundation. Upper Saddle River, NJ: Prentice-Hall; 1999;
  20. Heckerling PS, Gerber BS, Tape TG, Wigton RS. Use of genetic algorithms for neural networks to predict community-acquired pneumonia. Artificial Intel Med. 2004;30:71–84
  21. Inzitari D, Eliasziw M, Gates P, et al. The causes and risk of stroke in patients with asymptomatic internal carotid artery stenosis. N Engl J Med. 2000;342:1693–1700
  22. Jain AK, Duin RPW, Mao J. Statistical pattern recognition: A review. IEEE Trans Pattern Anal Machine Intell. 2000;22:4–37
  23. Kim HT, Kim BY, Park EH, et al. Computerized recognition of Alzheimer disease-EEG using genetic algorithms and neural network. Future Generation Computer Systems. 2005;21(7):1124–1130
  24. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intel Med. 2001;23:89–109
  25. Laws KI. In: Rapid texture identification. 238:USA: San Diego, CA; 1980;p. 376–380Proceedings of the SPIE Conference for Missile Guidance.
  26. Lisboa PJG. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Networks. 2002;15(1):11–39
  27. Mougiakakou SG, Golemati S, Gousias I, Nikita KS, Nicolaides AN. Computer-aided diagnosis of carotid atherosclerosis using Laws’ texture features and a hybrid trained neural network. 2003;Proceedings of the Engineering in Medicine and Biology Conference 2003 (EMBC’03), IEEE, Mexico, September
  28. North American Symptomatic Carotid Endarterectomy Trial Collaborators. Benefit of carotid endarterectomy in patients with symptomatic moderate or severe stenosis. N Engl J Med. 1998;339:1415–1425
  29. Polak JF, Shemanski L, O’Leary DH, et al. Cardiovascular Health Study Hypoechoic plaque at US of the carotid artery: an independent risk factor for incident stroke in adults aged 65 years or older. Radiology. 1998;208:649–654
  30. Polakowski WE, Cournoyer DA, Rogers SK, et al. Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency. IEEE Trans Med Imaging. 1997;16:811–819
  31. Southard TE, Southard KA. Detection of simulated osteoporosis in maxillae using radiographic texture analysis. IEEE Trans Biomed Eng. 1996;43:123–132
  32. Tegos TJ, Kalodiki E, Daskalopoulou SS, Nicolaides AN. Stroke: Epidemiology, clinical picture, and risk factors—Part I of III. Angiology. 2000;51:793–808
  33. Tegos TJ, Stavropoulos P, Sabetai MM, Khodabakhsh P, Sassano A, Nicolaides AN. Determinants of carotid plaque instability: Echoicity versus heterogeneity. Eur J Vasc Endovasc Surg. 2001;22:22–30
  34. Vince DG, Dixon KJ, Cothren RM, Cornhill JF. Comparison of texture analysis methods for the characterization of coronary plaques in intravascular ultrasound images. Comput Med Imaging Graph. 2000;24:221–229
  35. Wilhjelm JE, Grønholdt MLM, Wiebe B, Jespersen SK, Hansen LK, Sillesen H. Quantitative analysis of ultrasound B-mode images of carotid atherosclerotic plaque: correlation with visual classification and histological examination. IEEE Trans Med Imaging. 1998;17:910–922
  36. Woods K, Bowyer KW. Generating ROC curves for artificial neural networks. IEEE Trans Med Imaging. 1997;16(3):329–337
  37. Wu CM, Chen YC, Hsieh KS. Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging. 1992;11:141–152
  38. Van Bemmel JH, Musen MA. Handbook of medical informatics. Heidelberg, Germany: Springer Verlag; 1997;
  39. Yao X. An empirical study of genetic operators in genetic algorithms. Microprocessing and Microprogramming. 1993;38(1/5):707–714
  40. Zhang P, Verma Brijesh , Kumar K. Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection. Pattern Recog Lett. 2005;26:909–919

PII: S0301-5629(06)01774-1

doi: 10.1016/j.ultrasmedbio.2006.07.032

Ultrasound in Medicine and Biology
Volume 33, Issue 1 , Pages 26-36 , January 2007

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