Ultrasound in Medicine and Biology
Volume 35, Issue 10 , Pages 1607-1614 , October 2009

Computer-Aided Diagnosis for Breast Tumors by Using Vascularization of 3-D Power Doppler Ultrasound

  • Yu-Len Huang

      Affiliations

    • Department of Computer Science and Information Engineering, Tunghai University, Taichung, Taiwan
    • Corresponding Author InformationAddress correspondence to: Yu-Len Huang, Department of Computer Science and Information Engineering, Tunghai University, 181, Taichung-Kang Road, Sec. 3, Taichung 407, Taiwan.
    • ,
  • Shou-Jen Kuo

      Affiliations

    • Comprehensive Breast Cancer Center, Cancer Research Center, Changhua Christian Hospital, Changhua, Taiwan
    • ,
  • Chia-Chia Hsu

      Affiliations

    • Department of Computer Science and Information Engineering, Tunghai University, Taichung, Taiwan
    • ,
  • Hsin-Shun Tseng

      Affiliations

    • Comprehensive Breast Cancer Center, Cancer Research Center, Changhua Christian Hospital, Changhua, Taiwan
    • ,
  • Yi-Hsuan Hsiao

      Affiliations

    • Comprehensive Breast Cancer Center, Cancer Research Center, Changhua Christian Hospital, Changhua, Taiwan
    • ,
  • Dar-Ren Chen

      Affiliations

    • Comprehensive Breast Cancer Center, Cancer Research Center, Changhua Christian Hospital, Changhua, Taiwan
    • Corresponding Author InformationDar-Ren Chen, Comprehensive Breast Cancer Center, Laboratory of Cancer Research, Changhua Christian Hospital, 135 Nanhsiao Street, Changhua 500, Taiwan.

Received 16 May 2008 ,Revised 12 May 2009 ,Accepted 18 May 2009.

References 

  1. Breast Cancer Facts and Figures 2003–2004. American Cancer Society 2005.
  2. Blohmer JU, Oellinger H, Schmidt C, Hufnagl P, Felix R, Lichtenegger W. Comparison of various imaging methods with particular evaluation of color Doppler sonography for planning surgery for breast tumors. Arch Gynecol Obstet. 1999;262:159–171
  3. Carson PL, Moskalik AP, Govil A, Roubidoux MA, Fowlkes JB, Normolle D, et al. The 3-D and 2-D color flow display of breast masses. Ultrasound Med Biol. 1997;23:837–849
  4. Chang RF, Huang SF, Moon WK, Lee YH, Chen DR. Computer algorithm for analysing breast tumor angiogenesis using 3-D power Doppler ultrasound. Ultrasound Med Biol. 2006;32:1499–1508
  5. Chen DR, Chang RF, Chen WM, Moon WK. Computer-aided diagnosis for 3-dimensional breast ultrasonography. Arch Surg. 2003;138:296–302
  6. Chen DR, Chang RF, Wu WJ, Moon WK, Wu WL. 3-D breast ultrasound segmentation using active contour model. Ultrasound Med Biol. 2003;29:1017–1026
  7. Christianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. United Kingdom: Cambridge University Press; 2000;
  8. Cosgrove DO, Bamber JC, Davey JB, McKinna JA, Sinnett HD. Color Doppler signals from breast tumors. Work in progress. Radiology. 1990;176:175–180
  9. El Naqa I, Yang YY, Wernick MN, Galatsanos NP, Nishikawa RM. A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging. 2002;21:1552–1563
  10. Folkman J. What is the evidence that tumors are angiogenesis dependent?. J Natl Cancer Inst. 1990;82:4–6
  11. Folkman J. Tumor angiogenesis. Adv Cancer Res. 1985;43:175–203
  12. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36
  13. Haykin S. Support vector machines. Neural networks: A comprehensive foundation. Upper Saddle River, NJ: Prentice Hall; 1998;318–348
  14. Hsiao YH, Kuo SJ, Liang WM, Huang YL, Chen DR. Intra-tumor flow index can predict the malignant potential of breast tumor: Dependent on age and volume. Ultrasound Med Biol. 2008;34:88–95
  15. Huang YL, Wang KL, Chen DR. Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl. 2006;15:164–169
  16. Huang YL, Chen DR, Jiang YR, Kuo SJ, Wu HK, Moon WK. Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound. Ultrasound Obstet Gynecol. 2008;32(4):565–572
  17. Huang YL, Chen JH, Shen WC. Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images. Acad Radiol. 2006;13:713–720
  18. Jarvela IY, Sladkevicius P, Kelly S, Ojha K, Nargund G, Campbell S. Three-dimensional sonographic and power Doppler characterization of ovaries in late follicular phase. Ultrasound Obstet Gynecol. 2002;20:281–285
  19. Kim KI, Jung K, Park SH, Kim HJ. Support vector machines for texture classification. IEEE Trans Patt Anal Machine Intell. 2002;24:1542–1550
  20. Lee SW, Choi HY, Baek SY, Lim SM. Role of color and power Doppler imaging in differentiating between malignant and benign solid breast masses. J Clin Ultrasound. 2002;30:459–464
  21. Madjar H. Breast examinations with continuous wave and color Doppler. Ultrasound Obstet Gynecol. 1992;2:215–220
  22. Madjar H, Sauerbrei W, Prompeler HJ, Wolfarth R, Gufler H. Color Doppler and duplex flow analysis for classification of breast lesions. Gynecol Oncol. 1997;64:392–403
  23. McNicholas MM, Mercer PM, Miller JC, McDermott EW, O'Higgins NJ, MacErlean DP. Color Doppler sonography in the evaluation of palpable breast masses. AJR Am J Roentgenol. 1993;161:765–771
  24. Metz CE. ROC methodology in radiologic imaging. Invest Radiol. 1986;21:720–733
  25. Obuchowski NA. Receiver operating characteristic curves and their use in radiology. Radiology. 2003;229:3–8
  26. Riccabona M, Nelson TR, Pretorius DH. Three-dimensional ultrasound: Accuracy of distance and volume measures. Ultrasound Obstet Gynecol. 1996;7:429–434
  27. Sehgal CM, Arger PH, Rowling SE. Quantitative vascularity of breast masses by Doppler imaging: Regional variations and diagnostic implications. J Ultrasound Med. 2000;19:427–440
  28. Song MH, Breneman CM, Bi JB, Sukumar N, Bennett KP, Cramer S, et al. Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J Chem Inf Comput Sci. 2002;42:1347–1357
  29. Song Q, Hu WJ, Xie WF. Robust support vector machine with bullet hole image classification. IEEE Trans Syst Man Cybernet Part C: Appl Rev. 2002;32:440–448
  30. Sun YF, Fan XD, Li YD. Identifying splicing sites in eukaryotic RNA: Support vector machine approach. Comput Biol Med. 2003;33:17–29
  31. Vapnik V. Staistical learning theory. New York: John Wiley and Sons; 1998;
  32. Weiss SM, Kapouleas I. An empirical comparison of pattern recognition neural nets and machine learning classification methods. Proc 11th Int Joint Conf Artif Intell. Detroit, MI: Morgan Kaufmann. 1989;234–237
  33. Wright IA, Pugh ND, Lyons K, Webster DJ, Mansel RE. Power Doppler in breast tumeurs: A comparison with conventional Colour Doppler imaging. Eur J Ultrasound. 1998;7:175–181

PII: S0301-5629(09)00240-3

doi: 10.1016/j.ultrasmedbio.2009.05.014

Ultrasound in Medicine and Biology
Volume 35, Issue 10 , Pages 1607-1614 , October 2009

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