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Articles

Towards cost reduction of breast cancer diagnosis using mammography texture analysis

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Pages 385-402 | Received 13 May 2014, Accepted 02 Feb 2015, Published online: 22 Apr 2015

References

  • Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46, 175–185.
  • Bellotti, R., De Carlo, F., Tangaro, S., Gargano, G., Maggipinto, G., Castellano, M., … De Nunzio, G. (2006). A completely automated CAD system for mass detection in a large mammographic database. Medical Physics, 33, 3066–3075. doi:10.1118/1.2214177.
  • Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140.
  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. doi:10.1023/A:1010933404324.
  • Chang, C. C., & Lin, C.-J. (2011). Libsvm: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2, 1–27.
  • Chen, J., Kellokumpu, V., Zhao, G., & Pietikäinen, M. (2013). Rlbp: Robust local binary pattern. In Proceedings of the British machine vision conference (BMVC 2013) (pp. 122.1–122.10). Bristol, UK: BMVA Press.
  • Christoyianni, I., Koutras, A., Dermatas, E., & Kokkinakis, G. (2002). Computer aided diagnosis of breast cancer in digitized mammograms. Computerized Medical Imaging and Graphics, 26, 309–319. doi:10.1016/S0895-6111(02)00031-9.
  • Clausi, D. A. (2002). An analysis of co-occurrence texture statistics as a function of grey level quantization. Canadian Journal of Remote Sensing, 28, 45–62. doi:10.5589/m02-004.
  • Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE computer society conference on computer vision and pattern recognition, 2005 (Vol. 1, pp. 886–893). San Diego, CA: IEEE Computer Society.
  • de Oliveira Martins, L., Silva, A. C., De Paiva, A. C., & Gattass, M. (2009). Detection of breast masses in mammogram images using growing neural gas algorithm and ripley's k function. Journal of Signal Processing Systems, 55, 77–90. doi:10.1007/s11265-008-0209-3.
  • Elmore, J. G., Armstrong, K., Lehman, C. D., & Fletcher, S. W. (2005). Screening for breast cancer. Journal of the American Medical Association, 293, 1245–1256. doi:10.1001/jama.293.10.1245.
  • García-Manso, A., García-Orellana, C., González-Velasco, H., Gallardo-Caballero, R., & Macías-Macías, M. (2013). Study of the effect of breast tissue density on detection of masses in mammograms. Computational and Mathematical Methods in Medicine 2013, 2013, 1–10.
  • Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3, 610–621. doi:10.1109/TSMC.1973.4309314.
  • Heikkilä, M., Pietikäinen, M., & Schmid, C. (2009). Description of interest regions with local binary patterns. Pattern Recognition, 42, 425–436.
  • Iakovidis, D. K., Keramidas, E. G., & Maroulis, D. (2008). Fuzzy local binary patterns for ultrasound texture characterization. In Image analysis and recognition image analysis and recognition (pp. 750–759). Povoa de Varzim: Springer.
  • Jolliffe, I. (2005). Principal component analysis. Wiley Online Library: John Wiley & Sons.
  • Jones, J. P., & Palmer, L. A. (1987). An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology, 58, 1233–1258.
  • Lokate, M., Kallenberg, M. G., Karssemeijer, N., Van den Bosch, M. A., Peeters, P. H., & Van Gils, C. H. (2010). Volumetric breast density from full-field digital mammograms and its association with breast cancer risk factors: a comparison with a threshold method. Cancer Epidemiology Biomarkers & Prevention, 19, 3096–3105. doi:10.1158/1055-9965.EPI-10-0703.
  • Malvezzi, M., Bertuccio, P., Levi, F., La Vecchia, C., & Negri, E. (2014). European cancer mortality predictions for the year 2014. Annals of Oncology, 25, 1650–1656. Retrieved from http://annonc.oxfordjournals.org/content/early/2014/04/22/annonc.mdu138.abstract.
  • Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971–987. doi:10.1109/TPAMI.2002.1017623.
  • Oliver, A., Lladó, X., Freixenet, J., & Martí, J. (2007). False positive reduction in mammographic mass detection using local binary patterns. In Medical image computing and computer-assisted intervention, MICCAI 2007 (pp. 286–293). Brisbane: Springer.
  • Pomponiu, V., Hariharan, H., Zheng, B., & Gur, D. (2014). Improving breast mass detection using histogram of oriented gradients. In SPIE medical imaging (pp. 90351R–90351R). San Diego, CA: SPIE.
  • Qian, W., Li, L., Clarke, L., Clark, R. A., & Thomas, J. (1999). Digital mammography: comparison of adaptive and nonadaptive CAD methods for mass detection. Academic Radiology, 6, 471–480. doi:10.1016/S1076-6332(99)80166-4.
  • Ramirez Rivera, A., Castillo, R., & Chae, O. (2013). Local directional number pattern for face analysis: Face and expression recognition. IEEE Transactions on Image Processing, 22, 1740–1752. doi:10.1109/TIP.2012.2235848.
  • Scholkopft, B., & Mullert, K.-R. (1999). Fisher discriminant analysis with kernels. IX Neural networks for signal processing, 41–48.
  • Soh, L.-K., & Tsatsoulis, C. (1999). Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on Geoscience and Remote Sensing, 37, 780–795. doi:10.1109/36.752194.
  • Soltanian-Zadeh, H., Rafiee-Rad, F., & Pourabdollah-Nejad, D. S. (2004). Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms. Pattern Recognition, 37, 1973–1986. doi:10.1016/j.patcog.2003.03.001.
  • Suckling, J., Astley, S. D., Betal, N. C., Dance, D. R., Kok, S.-L., Parker, J., … Taylor, P. (1994). The mammographic image analysis society digital mammogram database. In 2nd international workshop on digital mammography (pp. 375–378). York: Excerta Medica.
  • Weldon, T. P., Higgins, W. E., & Dunn, D. F. (1996). Efficient Gabor filter design for texture segmentation. Pattern Recognition, 29, 2005–2015. doi:10.1016/S0031-3203(96)00047-7.
  • Zheng, Y. (2010). Breast cancer detection with Gabor features from digital mammograms. Algorithms, 3, 44–62. doi:10.3390/a3010044.
  • Zwiggelaar, R. (2010). Local greylevel appearance histogram based texture segmentation. In 10th international workshop on digital mammography (IWDM) (pp. 175–182). Girona, Catalonia, Spain.

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