Supervised Texture Classification for Intravascular Tissue Characterization

1. Wickline, S., Beyond intravascular imaging: Quantitative ultrasonic tissue characterization of vascular pathology, In: IEEE Ultrasonics symposium, 1994, pp. 1589–1597.

   Google Scholar 

2. Arul, P. and Amin, V., Characterization of beef muscle tissue using texture analysis of ultrasonic images, In: Proceedings of the Twelfth Southern Biomedical Engineering Conference, 1993, pp. 141–143.

   Google Scholar 

3. Mojsilovic, A. and Popovic, M., Analysis and characterization of myocardial tissue with the wavelet image extension [US images], In: Image Processing, 1995 (Proceedings) Vol. 2, pp. 23–26, 1995.

   Google Scholar 

4. Jin, X. and Ong, S., Fractal characterization of kidney tissue sections, In: Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers, Proceedings of the 16th Annual International Conference of the IEEE, Vol. 2, pp. 1136–1137, 1994.

   Google Scholar 

5. Cohen, F. and Zhu, Q., Quantitative soft-tissue characterization in human organs using texture/attenuation models, In: Proceedings in Multidimensional Signal Processing Workshop, 1989, pp. 47–48.

   Google Scholar 

6. Mavromatis, S. and Boi, J., Medical image segmentation using texture directional features, In: Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, Vol. 3, pp. 2673–2676, 2001.

   Article  Google Scholar 

7. Mavromatis, S., Mammographic mass classification using textural features and descriptive diagnostic data, In: Digital Signal Processing, DSP 2002. 14th International Conference on, Vol. 1, pp. 461–464, 2002.

   Google Scholar 

8. Donohue, K. and Forsberg, F., Analysis and classification of tissue with scatterer structure templates, IEEE Trans. Ultrasonics, Ferroelect. Frequency Control, Vol. 46, No. 2, pp. 300–310, 1999.

   Article  Google Scholar 

9. Ravizza, P., Myocardial tissue characterization by means of nuclear magnetic resonance imaging, In: Computers in Cardiology 1991 (Proceedings), pp. 501–504.

   Google Scholar 

10. Vandenberg, J., Arterial imaging techniques and tissue characterization using fuzzy logic, In: Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, 1994, pp. 239–243.

    Google Scholar 

11. Nailon, W. and McLaughlin, S., Comparative study of textural analysis techniques to characterize tissue from intravascular ultrasound, In: Proceedings of the IEEE International Conference of Image Processing, Switzerland, IEEE Signal Processing Society, Piscataway, NJ, 1996, pp. 303–305.

    Google Scholar 

12. Nailon, W. and McLaughlin, S., Intravascular ultrasound image interpretation, In: Proceedings of the International Conference on Pattern Recognition, Austria, IEEE Computer Society Press, Los Alamitos, CA, 1996, pp. 503–506.

    Chapter  Google Scholar 

13. Nailon, W., Fractal texture analysis: An aid to tissue characterization with intravascular ultrasound, In: Proceedings 19th International Conference, IEEE/EMBS, 1997, pp. 534–537.

    Google Scholar 

14. Spencer, T., Characterization of atherosclerotic plaque by spectral analysis of 30mhz intravascular ultrasound radio frequency data, In: IEEE Ultrasonics Symposium, 1996, pp. 1073–1076.

    Google Scholar 

15. Dixon, K., Characterization of coronary plaque in intravascular ultrasound using histological correlation, In: 19th International Conference, IEEE/EMBS, pp. 530–533, 1997.

    Google Scholar 

16. Ahmed, M. and Leyman, A., Tissue characterization using radial transform and higher order statistics, In: Nordic Signal Processing Symposium, 2000, pp. 13–16.

    Google Scholar 

17. de Korte, C. L. and van der Steen, A. F. W., Identification of atherosclerotic plaque components with intravascular ultrasound elastography in vivo: A yucatan pig study, Circulation, Vol. 105, No. 14, pp. 1627–1630, 2002.

    Article  Google Scholar 

18. Zhang, X. and Sonka, M., Tissue characterization in intravascular ultrasound images, IEEE Trans. Med. Imaging, Vol. 17, No. 6, pp. 889–899, 1998.

    Article  Google Scholar 

19. Pujol, O. and Radeva, P., Automatic segmentation of lumen in intravascular ultrasound images: An evaluation of texture feature extractors, In: Proceedings for IBERAMIA, 2002, pp. 159–168.

    Google Scholar 

20. Pujol, O. and Radeva, P., Near real time plaque segmentation of ivus, In: Proceedings of Computers in Cardiology, 2003, pp. 159–168.

    Google Scholar 

21. Randen, T. and Husoy, J. H., Filtering for texture classification: A comparative study, Pattern Recogn., Vol. 21, No. 4, pp. 291–310, 1999.

    Google Scholar 

22. Haralick, R., Shanmugam, K., and Dinstein, I., Textural features for image classification, IEEE Trans. System, Man, Cybernetics, Vol. 3, pp. 610–621, 1973.

    Google Scholar 

23. Tuceryan, M., Moment based texture segmentation, Pattern Recogn. Lett., Vol. 15, pp. 659–668, 1994.

    Article  Google Scholar 

24. Lindeberg, T., Scale-Space Theory in Computer Vision, Kluwer, Dordrecht, Netherlands, 1994.

    Google Scholar 

25. Jain, A. and Farrokhnia, F., Unsupervised texture segmentation using gabor filters, In: Systems, Man and Cybernetics, 1990 (Conference Proceedings), pp. 14–19.

    Google Scholar 

26. Mallat, S., A theory for multiresolution signal decomposition: The wavelet representation, IEEE Trans. Pattern Anal. Machine Intell., Vol. 11, No. 7, pp. 674–694, 1989.

    Article  MATH  Google Scholar 

27. Mandelbrot, B., The Fractal Geometry of Nature, W. H. Freeman, New York, 1983.

    Google Scholar 

28. Ojala, T., Pietikainen, M., and Maenpaa, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Machine Intell., Vol. 24, No. 7, pp. 971–987, 2002.

    Article  Google Scholar 

29. Julesz, B., Visual pattern discrimination, IRE Trans. Inf. Theory, Vol. IT-8, pp. 84–92, 1962.

    Article  Google Scholar 

30. Ohanian, P. and Dubes, R., Performance evaluation for four classes of textural features, Pattern Recogn., Vol. 25, No. 8, pp. 819–833, 1992.

    Article  Google Scholar 

31. Martinez, J. and Thomas, F., Efficient computation of local geometric moments, IEEE Trans. Image Process., Vol. 11, No. 9, pp. 1102–1111, 2002.

    Article  Google Scholar 

32. Caelli, T. and Oguztoreli, M. N., Some tasks and signal dependent rules for spatial vision, Spatial Vision, No. 2, pp. 295–315, 1987.

    Google Scholar 

33. Chaudhuri, B. and Sarkar, N., Texture segmentation using fractal dimension, IEEE Trans. Pattern Anal. Machine Intell., Vol. 17, No. 1, pp. 72–77, 1995.

    Article  Google Scholar 

34. Lindeberg, T., Scale-space theory: A basic tool for analysing structures at different scales, J. Appl. Stat., Vol. 21, No. 2, pp. 225–270, 1994.

    Google Scholar 

35. Rao, R. and Ballard, D., Natural basis functions and topographic memory for face recognition, In: Proceedings of International Joint Conference on Artificial Intelligence, 1995, pp. 10–17.

    Google Scholar 

36. Lumbreras, F., Segmentation, Classification and Modelization of Textures by means of Multiresolution Descomposition Techniques, PhD Thesis, Computer Vision Center, Universitat Autònoma de Barcelona, 2001.

    Google Scholar 

37. Jain, A. and Farrokhnia, F., Amulti-channel filtering approach to texture segmentation, In: Proceedings of Computer Vision and Pattern Recognition, CVPR’91, 1991, pp. 364–370.

    Google Scholar 

38. Fukunaga, K., Introduction to Statistical Pattern Recognition, Academic Press, New York, 1971.

    Google Scholar 

39. Belhumeur, P., Eigenfaces vs fisherfaces: Recognition using class specific linear projection, IEEE Pattern Analy. Machine Intell., Vol. 19, No. 7, pp. 711–720, 1997.

    Article  Google Scholar 

40. Schapire, R. E., The boosting approach to machine learning. An overview, In: MSRI Workshop on Nonlinear Estimation and Classifi-cation, 2002.

    Google Scholar 

41. Viola, P. and Jones, M., Rapid object detection using a boosted cascade of simple features, In: Conference on Computer Vision and Pattern Recognition, 2001, pp. 511–518.

    Google Scholar 

42. Duda, R. and Hart, P., Pattern Classification, Wiley Inter Science, New York, 2001. Second Edition.

    MATH  Google Scholar 

43. Sonka, M. and Zhang, X., Segmentation of intravascular ultrasound images: A knowledge-based approach, IEEE Trans. Med. Imaging, Vol. 17, No. 6, pp. 889–899, 1998.

    Article  Google Scholar 

44. von Birgelen, C., Computerized assessment of coronary lumen and atherosclerotic plaque dimensions in three-dimensional intravascular ultrasound correlated with histomorphometry, Am. J. Cardiol., Vol. 78, pp. 1202–1209, 1996.

    Article  Google Scholar 

45. Klingensmith, J., Shekhar, R., and Vince, D., Evaluation of threedimensional segmentation algorithms for identification of luminal and medial-adventitial borders in intravascular ultrasound images, IEEE Trans. Med. Imaging, Vol. 19, No. 10, pp. 996–1011, 2000.

    Article  Google Scholar 

46. McInerney, T. and Terzopoulos, D., Deformable models in medical images analysis: A survey, Med. Image Anal., Vol. 1, No. 2, pp. 91–108, 1996.

    Article  Google Scholar 

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