Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 914091, 6 pages
http://dx.doi.org/10.1155/2015/914091
Research Article

Computer-Aided Assessment of Tumor Grade for Breast Cancer in Ultrasound Images

1Comprehensive Breast Cancer Center, Department of Medical Research, Changhua Christian Hospital, 135 Nanhsiao Street, Changhua 50006, Taiwan
2Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan

Received 14 August 2014; Accepted 10 February 2015

Academic Editor: Issam El Naqa

Copyright © 2015 Dar-Ren Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Jemal, R. Siegel, E. Ward et al., “Cancer statistics, 2006,” CA: A Cancer Journal for Clinicians, vol. 56, no. 2, pp. 106–130, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. E. A. Rakha, J. S. Reis-Filho, F. Baehner et al., “Breast cancer prognostic classification in the molecular era: the role of histological grade,” Breast Cancer Research, vol. 12, no. 4, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Karellas and S. Vedantham, “Breast cancer imaging: a perspective for the next decade,” Medical Physics, vol. 35, no. 11, pp. 4878–4897, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Nothacker, V. Duda, M. Hahn et al., “Early detection of breast cancer: benefits and risks of supplemental breast ultrasound in asymptomatic women with mammographically dense breast tissue. A systematic review,” BMC Cancer, vol. 9, article 335, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. V. Corsetti, N. Houssami, M. Ghirardi et al., “Evidence of the effect of adjunct ultrasound screening in women with mammography-negative dense breasts: interval breast cancers at 1 year follow-up,” European Journal of Cancer, vol. 47, no. 7, pp. 1021–1026, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. H. D. Cheng, J. Shan, W. Ju, Y. Guo, and L. Zhang, “Automated breast cancer detection and classification using ultrasound images: a survey,” Pattern Recognition, vol. 43, no. 1, pp. 299–317, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. C. W. Elston and I. O. Ellis, “Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up,” Histopathology, vol. 19, no. 5, pp. 403–410, 1991. View at Publisher · View at Google Scholar · View at Scopus
  8. P. M. Lamb, N. M. Perry, S. J. Vinnicombe, and C. A. Wells, “Correlation between ultrasound characteristics, mammographic findings and histological grade in patients with invasive ductal carcinoma of the breast,” Clinical Radiology, vol. 55, no. 1, pp. 40–44, 2000. View at Publisher · View at Google Scholar · View at Scopus
  9. S. H. Kim, B. K. Seo, J. Lee et al., “Correlation of ultrasound findings with histology, tumor grade, and biological markers in breast cancer,” Acta Oncologica, vol. 47, no. 8, pp. 1531–1538, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Aho, A. Irshad, S. J. Ackerman et al., “Correlation of sonographic features of invasive ductal mammary carcinoma with age, tumor grade, and hormone-receptor status,” Journal of Clinical Ultrasound, vol. 41, no. 1, pp. 10–17, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Wojcinski, N. Stefanidou, P. Hillemanns, and F. Degenhardt, “The biology of malignant breast tumors has an impact on the presentation in ultrasound: an analysis of 315 cases,” BMC Women's Health, vol. 13, no. 1, article 47, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. American College of Radiology, Breast Imaging Reporting and Data System (BI-RADS) Ultrasound, American College of Radiology, Reston, Va, USA, 1st edition, 2003.
  13. C.-Y. Chang, S.-J. Kuo, H.-K. Wu, Y.-L. Huang, and D.-R. Chen, “Stellate masses and histologic grades in breast cancer,” Ultrasound in Medicine and Biology, vol. 40, no. 5, pp. 904–916, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Irshad, R. Leddy, E. Pisano et al., “Assessing the role of ultrasound in predicting the biological behavior of breast cancer,” The American Journal of Roentgenology, vol. 200, no. 2, pp. 284–290, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. P. A. Yushkevich, J. Piven, H. C. Hazlett et al., “User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability,” NeuroImage, vol. 31, no. 3, pp. 1116–1128, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. V. Caselles, F. Catté, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numerische Mathematik, vol. 66, no. 1, pp. 1–31, 1993. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” International Journal of Computer Vision, vol. 22, no. 1, pp. 61–79, 1997. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Osher and J. A. Sethian, “Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations,” Journal of Computational Physics, vol. 79, no. 1, pp. 12–49, 1988. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. J. A. Sethian, Level Set Methods and Fast Marching Methods, 1999.
  20. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973. View at Publisher · View at Google Scholar · View at Scopus
  21. W. Chen, M. L. Giger, H. Li, U. Bick, and G. M. Newstead, “Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images,” Magnetic Resonance in Medicine, vol. 58, no. 3, pp. 562–571, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Jobanputra and D. A. Clausi, “Texture analysis using Gaussian weighted grey level co-occurrence probabilities,” in Proceedings of the 1st Canadian Conference on Computer and Robot Vision, pp. 51–57, May 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. Y.-L. Huang, D.-R. Chen, Y.-R. Jiang, S.-J. Kuo, H.-K. Wu, and W. K. Moon, “Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound,” Ultrasound in Obstetrics and Gynecology, vol. 32, no. 4, pp. 565–572, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. W. K. Moon, Y.-W. Shen, C.-S. Huang, L.-R. Chiang, and R.-F. Chang, “Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images,” Ultrasound in Medicine and Biology, vol. 37, no. 4, pp. 539–548, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. E. Bribiesca, “An easy measure of compactness for 2D and 3D shapes,” Pattern Recognition, vol. 41, no. 2, pp. 543–554, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Sahiner, H. P. Chan, M. A. Roubidoux et al., “Computerized characterization of breast masses on three-dimensional ultrasound volumes,” Medical Physics, vol. 31, no. 4, pp. 744–754, 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. P. S. Rodrigues, G. A. Giraldi, M. Provenzano, M. D. Faria, R.-F. Chang, and J. S. Suri, “A new methodology based on q-entropy for breast lesion classification in 3-D ultrasound images,” in Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1048–1051, September 2006. View at Publisher · View at Google Scholar · View at Scopus
  28. W.-C. Shen, R.-F. Chang, W. K. Moon, Y.-H. Chou, and C.-S. Huang, “Breast ultrasound computer-aided diagnosis using BI-RADS features,” Academic Radiology, vol. 14, no. 8, pp. 928–939, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. P. E. Palmer, Manual of Diagnostic Ultrasound, World Health Organization, Geneva, Switzerland, 1995.
  30. G. Dougherty, Digital Image Processing for Medical Applications, Cambridge University Press, Cambridge,UK, 2009.
  31. A. Ben-Hur and J. Weston, “A user's guide to support vector machines,” Methods in Molecular Biology (Clifton, N.J.), vol. 609, pp. 223–239, 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. C.-C. Chang and C.-J. Lin, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. C.-L. Huang and C.-J. Wang, “A GA-based feature selection and parameters optimizationfor support vector machines,” Expert Systems with Applications, vol. 31, no. 2, pp. 231–240, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  35. J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (ROC) curve,” Radiology, vol. 143, no. 1, pp. 29–36, 1982. View at Publisher · View at Google Scholar · View at Scopus
  36. M. H. Zweig and G. Campbell, “Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine,” Clinical Chemistry, vol. 39, no. 4, pp. 561–577, 1993. View at Google Scholar · View at Scopus
  37. S. H. Park, J. M. Goo, and C.-H. Jo, “Receiver operating characteristic (ROC) curve: practical review for radiologists,” Korean Journal of Radiology, vol. 5, no. 1, pp. 11–18, 2004. View at Publisher · View at Google Scholar · View at Scopus