104
Views
5
CrossRef citations to date
0
Altmetric
Medical Electronics

Automated Computer Aided Diagnosis Using Altered Multi-Phase Level Sets in Application to Categorize the Breast Cancer Biopsy Images

ORCID Icon, ORCID Icon &

References

  • I. Humayun, V. Antoine, R. Ludovic, and R. Daniel, “Methods for nuclei detection, segmentation and classification in digital histopathology: A review. current status and future potential,” IEEE Rev Biomed Eng., Vol. 7, pp. 97–114, 2013.
  • A. D. Belsare, and M. M. Mushrif, “Histopathological image analysis using image processing techniques: An overview,” Signal & Image Processing: An International Journal (SIPIJ), Vol. 3, no. 4, pp. 23–36, 2012.
  • D. Jayadevappa, S. Srinivas Kumar, and D. S. Murthy, “Medical image segmentation algorithms using deformable models: A review,” IETE Tech. Rev., Vol. 28, no. 3, pp. 248–55, 2011.
  • S. Punitha, A. Amuthan, and K. Suresh Joseph, “Benign and malignant breast cancer segmentation using optimized region growing technique,” Future Computing Inform J, Vol. 3, no. 2, pp. 348–58, 2018.
  • M. A. Aswathy, and M. Jagannath, “Detection of breast cancer on digital histopathology images: Present status and future possibilities,” Informatics in Medicine Unlocked, Vol. 8, pp. 74–79, 2017.
  • Z. Han, B. Wei, Y. Zheng, Y. Yin, K. Li, and S. Li, “Breast cancer multi-classification from histopathological images with structured deep learning model,” Sci. Rep, Vol. 7, no. 4172, pp. 1–10, 2017.
  • Y. M. George, H. H. Zayed, M. I. Roushdy, and B. M. Elbagoury, “Remote computer-aided breast cancer detection and diagnosis system based on cytological images,” IEEE Syst. J., Vol. 8, no. 3, pp. 949–64, 2014.
  • G. Frederic, R. Fedkiw, and S. Osher, “A review of level-set methods and some recent applications,” J. Comp. Phys, Vol. 353, pp. 82–109, 2018.
  • C. Li, R. Huang, Z. Ding, C. J. Gatenby, D. N. Metaxas, and J. C. Gore, “A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI,” IEEE Trans. Image Process., Vol. 20, no. 7, pp. 2007–16, 2011.
  • P. Ganasala, and V. Kumar, “CT and MR image fusion scheme in nonsubsampled contourlet transform domain,” J. Digit Imaging, Vol. 27, no. 3, pp. 407–18, 2014.
  • World Health Organization. Fact sheet No. 297: Cancer, http://www.who.int/mediacentre/factsheets/fs297/en/index.html, 2010.
  • B. Thomas, and D. Cremers, “On the statistical interpretation of the piecewise smooth Mumford-Shah functional,” in International Conference on Scale Space and Variational Methods in Computer Vision, Berlin, Heidelberg: Springer, 2007, pp. 203–213.
  • T. F. Chan, and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Process., Vol. 10, no. 2, pp. 266–77, 2001.
  • K. Tapas, M. M. David, S. N. Nathan, D. P. Christine, S. Ruth, and Y. W. Angela, “An efficient k-means clustering algorithm: Analysis and implementation,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 24, no. 7, pp. 881–92, 2002.
  • H. Maciej, K. Jozef, and O. Andrzej. “Hough transform, search strategy and watershed algorithm in segmentation of cytological images,” Computer Recognition Systems: Springer-Verlag Berlin Heidelberg 2, ASC 45: pp. 550–7, 2007.
  • A. Mouelhi, S. Mounir, F. Farhat, M. Karima, and B. R. Khaled, “Automatic image segmentation of nuclear stained breast tissue sections using color active contour model and an improved watershed method,” Biomed. Signal. Process. Control., Vol. 8, pp. 421–36, 2013.
  • F. Jianping, K. Y. Yau David, E. Ahmed, and K. Walid G, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Trans on Image Processing, Vol. 10, no. 10, pp. 1454–66, 2001.
  • U. Rajyalakshmi, S. Koteswararao, and K. Satyaprasad, “Supervised classification of breast cancer malignancy using integrated modified marker controlled watershed approach,” in IEEE Xplore-7th IEEE International Advan-ced Computing Conference (IACC-2017), Electronic ISSN, 2017, pp. 584–589.
  • F. Hussain, X. Jun, B. Ajay, B. Gyan, G. Shridar, and F. Michael, “Expectation–maximization-driven geodesic active contour with overlap resolution (EMaGACOR): Application to lymphocyte segmentation on breast cancer histopathology,” IEEE Trans. Biomed. Eng., Vol. 57, no. 7, pp. 1676–89, 2010.
  • E. P. Marina, N. Christophoros, and C. Antonia, “Combining shape, texture and intensity features for cell nuclei extraction in pap smear images,” Pattern Recogn Lett, Vol. 32, no. 6, pp. 838–53, 2011.
  • A. Sahirzeeshan, and M. Anant, “An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery,” IEEE Trans. Med. Imaging, Vol. 31, no. 7, pp. 1448–60, 2012.
  • M. Kanchanamani, and P. Varalakshmi, “Performance evaluation and comparative analysis of various machine learning techniques for diagnosis of breast cancer,” Biomed. Res., Vol. 27, no. 3, pp. 623–31, 2016.
  • U. Rajyalakshmi, S. Koteswararao, and K. Satyaprasad, “Integrated novel multi-phase level sets with modified marker controlled watershed for segmentation of breast cancer histopathological images,” J Adv Res Dynamical Control Syst (JARDCS), Vol. 9, no. 3, pp. 47–55, 2017.
  • K. Adem, S. Fatih, A. Hulya, and O. Figen, “Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samples,” Comput. Methods Programs Biomed., Vol. 110, no. 3, pp. 298–307, 2013.
  • R. Uppada, K. Sanagapallela, and S. Kodati, “Image automatic categorisation using selected features attained from integrated non-subsampled contourlet with multiphase level sets,” Def. Life Sci. J., Vol. 4, no. 1, pp. 67–75, 2018.
  • J. R. Quinlan, “Induction of decision trees,” I. Journal of Machine Learning, Vol. 1, pp. 81–106, 1986.
  • N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” Am. Stat., Vol. 46, no. 3, pp. 175–85, 1992.
  • R. Collobert, and S. Bengio. Links between perceptrons, MLPs and SVMs. Proc. Int’l Conf. on Machine Learning (ICML). 2004.
  • C. Hsu, and C. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Trans. Neural Netw., Vol. 13, no. 2, pp. 415–25, 2002.

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.