139
Views
3
CrossRef citations to date
0
Altmetric
Review Articles

Evaluation of Image Features Within and Surrounding Lesion Region for Risk Stratification in Breast Ultrasound Images

ORCID Icon, &

References

  • O. Faust, U. R. Acharya, K. M. Meiburger, Molinari, F., J. E. Koh, C. H. Yeong, and K. H. Ng “Comparative assessment of texture features for the identification of cancer in ultrasound images: a review,” Biocybernet. Biomed. Eng., Vol. 38, no. 2, pp. 275–96, 2018. doi: 10.1016/j.bbe.2018.01.001
  • L. Panigrahi, K. Verma, and B. K. Singh, “Hybrid segmentation method based on multi-scale Gaussian kernel fuzzy clustering with spatial bias correction and region-scalable fitting for breast US images,” IET Comput. Vision, Vol. 12, no. 8, pp. 1067–77, 2018. doi: 10.1049/iet-cvi.2018.5332
  • American Cancer Society, Cancer Facts & Figures 2016.
  • B. K. Singh, K. Verma, A. S. Thoke, and J. S. Suri, “Risk stratification of 2D ultrasound-based breast lesions using hybrid feature selection in machine learning paradigm,” Measurement, Vol. 105, pp. 146–57, 2017. doi: 10.1016/j.measurement.2017.01.016
  • Q. Huang, F. Yang, L. Liu, and X. Li, “Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis,” Informat. Sci., Vol. 314, pp. 293–310, 2015. doi: 10.1016/j.ins.2014.08.021
  • S. Joo, Y. S. Yang, W. K. Moon, and H. C. Kim, “Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features,” IEEE Trans. Med. Imaging, Vol. 23, no. 10, pp. 1292–1300, 2004. doi: 10.1109/TMI.2004.834617
  • R. Guo, G. Lu, B. Qin, and B. Fei, “Ultrasound imaging technologies for breast cancer detection and management: a review,” Ultrasound Med. Biol., Vol. 44, no. 1, pp. 37–70, 2018. doi: 10.1016/j.ultrasmedbio.2017.09.012
  • V. K. Sudarshan, M. R. K. Mookiah, U. R. Acharya, V. Chandran, F. Molinari, H. Fujita, and K. H. Ng, “Application of wavelet techniques for cancer diagnosis using ultrasound images: a Review,” Comput. Biol. Med., Vol. 69, pp. 97–111, 2016. doi: 10.1016/j.compbiomed.2015.12.006
  • D. Thigpen, A. Kappler, and R. Brem, “The role of ultrasound in screening dense breasts – a review of the literature and practical solutions for implementation,” Diagnostics, Vol. 8, no. 1, pp. 20, 2018. doi: 10.3390/diagnostics8010020
  • Y. L. Huang, S. J. Kuo, C. S. Chang, Y. K. Liu, W. K. Moon, and D. R. Chen, “Image retrieval with principal component analysis for breast cancer diagnosis on various ultrasonic systems,” Ultrasound Obstet. Gynecol., Vol. 26, pp. 558–66, 2005. doi: 10.1002/uog.1951
  • A. V. Alvarenga, W. C. Pereira, A. F. Infantosi, and C. M. Azevedo, “Classifying breast tumours on ultrasound images using a hybrid classifier and texture features,” In Intelligent Signal Processing, IEEE International Symposium, pp. 1–6, Oct. 2007 .
  • 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 Obstet. Gynecol., Vol. 32, no. 4, pp. 565–72, 2008. doi: 10.1002/uog.5205
  • M. H. Yap, E. A. Edirisinghe, and H. E. Bez, “A comparative study in ultrasound breast imaging classification,” Med Imaging, Vol. 7259, pp. 72591S, Mar. 2009.
  • W. C. A. Pereira, A. V. Alvarenga, A. F. C. Infantosi, L. Macrini, and C. E. Pedreira, “A non-linear morphometric feature selection approach for breast tumor contour from ultrasonic images,” Comput. Biol. Med., Vol. 40, pp. 912–8, 2010. doi: 10.1016/j.compbiomed.2010.10.003
  • Y. Su, Y. Wang, J. Jiao and Y. Guo, “Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features,” Open Med. Informat. J., Vol. 5 , no. Suppl 1-M3, pp. 26–37, 2011. doi: 10.2174/1874431101105010026
  • W. Gomez, W. C. A. Pereira, and A. F. C. Infantosi, “Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound,” IEEE Trans Med. Imaging, Vol. 31, no. 10, pp. 1889–99, 2012. doi: 10.1109/TMI.2012.2206398
  • W. J. Wu, S. W. Lin, and W. K. Moon, “Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images,” Comput. Med. Imaging Graph., Vol. 36, no. 8, pp. 627–33, 2012. doi: 10.1016/j.compmedimag.2012.07.004
  • S. Zhou, J. Shi, J. Zhu, Y. Cai and R. Wang, “Shearlet based texture feature extraction for classification of breast tumor in ultrasound image,” Biomed. Signal Process. Control, Vol. 8, no. 6, pp. 688–96, 2013. doi: 10.1016/j.bspc.2013.06.011
  • J. H. Kim, et al., “Computer–aided detection system for masses in automated whole breast ultrasonography: Development and evaluation of the effectiveness,” Ultrasonography, Vol. 33, no. 2, pp. 105–15, 2014. doi: 10.14366/usg.13023
  • Q. Zhang, et al., “ A computer aided system for classification of breast tumors in ultrasound images via biclustering learning,” in International Conference on Machine Learning and Cybernetics, Springer, Berlin, Heidelberg, 2014, pp. 24–32.
  • C. M. Lin, Y. L. Hou, T. Y. Chen and K. H. Chen, “Breast nodules computer aided diagnostic system design using fuzzy cerebellar model neural networks,” IEEE Trans. Fuzzy Syst., Vol. 22, no. 3, pp. 693–99, 2014. doi: 10.1109/TFUZZ.2013.2269149
  • B. K. Singh, K. Verma and A. S. Thoke, “Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images,” Expert Syst. Appl., Vol. 66, pp. 114–23, 2016. doi: 10.1016/j.eswa.2016.09.006
  • http://www.onlinemedicalimages.com/index.php/en/, accessed on 9/10/2017.
  • L. Panigrahi, K. Verma, B. K. Singh, “Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution,” Expert Syst. Appl., Vol. 115, pp. 486–98, 2019. doi: 10.1016/j.eswa.2018.08.013
  • S. Selvan and S. S. Devi, “Automatic seed point selection in ultrasound echography images of breast using texture features,” Biocybernet. Biomed. Eng., Vol. 35, no. 3, pp. 157–68, 2015. doi: 10.1016/j.bbe.2014.10.001
  • Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Processing, Vol. 11, pp. 1260–70, 2002. doi: 10.1109/TIP.2002.804276
  • B. K. Singh, K. Verma, L. Panigrahi, and A. S. Thoke, “Integrating radiologist feedback with computer aided diagnostic systems for breast cancer risk prediction in ultrasonic images: an experimental investigation in machine learning paradigm,” Expert Syst. Appl., Vol. 90, pp. 209–23, 2017. doi: 10.1016/j.eswa.2017.08.020
  • D. R. Chen, R. F. Chang and Y. L. Huang, “Breast cancer diagnosis using self organizing map for sonography,” Ultrasound Med. Biol., Vol. 26, no. 3, pp. 405–11, 2000. doi: 10.1016/S0301-5629(99)00156-8
  • S. Gefen, et al., “ROC analysis of ultrasound tissue characterization classifier for breast cancer diagnosis,” IEEE Trans. Med. Imaging, Vol. 22, no. 2, pp. 170–77, 2003. doi: 10.1109/TMI.2002.808361
  • X. Shi, H. D. Cheng, L. Hu, W. Ju, and J. Tian, “Detection and classification of masses in breast ultrasound images,” Digital Signal Process., Vol. 20, no. 3, pp. 824–36, 2010. doi: 10.1016/j.dsp.2009.10.010
  • T. Tan, B. Platel, H. Huisman, C. I. Sanchez, R. Mus, and N. Karssemeijer, “Computer aided lesion diagnosis in automated 3-D breast ultrasound using coronal speculation,” IEEE Trans. Med. Imaging, Vol. 31, no. 5, pp. 1034–42, 2012. doi: 10.1109/TMI.2012.2184549
  • L. Huang, J. Shi and R. Wang, “Shearlet-based ultrasound texture features forclassification of breast tumor,” in Seventh International Conference onInternet Computing for Engineering and Science, China, pp. 20–22, September 2013 .
  • J. A. Ali and J. Janet, “Discrete shearlet transform based classification of microcalcification in digital mammograms,” J. Comput. Appl., Vol. 6, no. 1, pp. 19–21, 2013.
  • J. A. Ali and J. Janet, “Mass classification in digital mammograms based on discrete shearlet transform,” J. Comput. Sci., Vol. 9, no. 6, pp. 726–32, 2013. doi: 10.3844/jcssp.2013.726.732
  • P. Wahdan, A. Saad and A. Shoukry, “Comparing classification techniques to detect breast tumor,” in Proceedings of the international conference on biomedical engineering and systems, Prague, Czech Republic, 2014, pp. 1–6.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.