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An Advanced Image Analysis Tool for the Quantification and Characterization of Breast Cancer in Microscopy Images

Journal of Medical Systems volume 39, Article number: 31 (2015) Cite this article

Abstract

The paper presents an advanced image analysis tool for the accurate and fast characterization and quantification of cancer and apoptotic cells in microscopy images. The proposed tool utilizes adaptive thresholding and a Support Vector Machines classifier. The segmentation results are enhanced through a Majority Voting and a Watershed technique, while an object labeling algorithm has been developed for the fast and accurate validation of the recognized cells. Expert pathologists evaluated the tool and the reported results are satisfying and reproducible.

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References

  1. German, R. R., Fink, A. K., Heron, M., Johnson, C. J., Finch, J. L., and Yin, D., The accuracy of cancer mortality group: The accuracy of cancer mortality statistics based on death certificates in the United States. Cancer Epidemiology 35(2):126–131, 2011.

    Article  Google Scholar 

  2. Loncaster, J., and Dodwell, D., Adjuvant radiotherapy in breast cancer. Are there factors that allow selection of patients who do not require adjuvant radiotherapy following breast-conserving surgery for breast cancer? Minerva Med. 93:101–107, 2002.

    Google Scholar 

  3. Hansen, C. M., Hamberg, K. J., Binderup, E., and Binderup, L., Seocalcitol (EB 1089): A vitamin D analogue of anticancer potential. Background, design, synthesis, preclinical and clinical evaluation. Curr. Pharm. Des. 6(7):803–828, 2000.

    Article  Google Scholar 

  4. Loukas, C. G., Wilson, G. D., Vojnovic, B., and Linney, A., An image analysis-based approach for automated counting of cancer cell nuclei in tissue sections. Cytometry Part A 55A(1):30–42, 2003.

    Article  Google Scholar 

  5. Saveliev P, Pahwa A.,Topology based method of segmentation of gray scale images. Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009, 2, pp 620–626, 2009.

  6. Phukpattaranont, P., Limsiroratana, S., and Boonyaphiphat, P., Computer-aided system for microscopic images: Application to breast cancer nuclei counting. Int. J. Appl. Biomed. Eng. 2(1):69–74, 2009.

    Google Scholar 

  7. Maglogiannis, I., Sarimveis, H., Kiranoudis, C. T., Chatzioannou, A. A., Oikonomou, N., and Aidinis, V., Radial basis function neural networks classification for the recognition of idiopathic pulmonary fibrosis in microscopic images. IEEE Trans. Inf. Technol. Biomed. 12(1):42–54, 2008.

    Article  Google Scholar 

  8. Tosun, A. B., and Gunduz-Demir, C., Graph run-length matrices for histopathological image segmentation. IEEE Trans. Med. Imaging 30(3):721–732, 2011.

    Article  Google Scholar 

  9. Issac Niwas, S., Palanisamy, P., Sujathan, K., and Bengtsson, E., Analysis of nuclei textures of fine needle aspirated cytology images for breast cancer diagnosis using complex Daubechies wavelets. Signal Process. 93(10):2828–2837, 2013.

    Article  Google Scholar 

  10. Kara, S., Okandan, M., Sener, F., and Yıldırım, M., Imaging system for visualization and numerical analysis of cancer at stomach and skin tissues. J. Med. Syst. 29(2):179–185, 2005.

    Article  Google Scholar 

  11. Chen, X., Zhou, X., and Wong, S. T., Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans. Biomed. Eng. 53(4):762–766, 2006.

    Article  Google Scholar 

  12. Lindblad, J., Wählby, C., Bengtsson, E., and Zaltsman, A., Image analysis for automatic segmentation of cytoplasms and classification of Rac1 activation. Cytometry Part A 57(1):22–33, 2004.

    Article  Google Scholar 

  13. Hiremath PS, Iranna YH., Automated cell nuclei segmentation and classification of squamous cell carcinoma from microscopic images of esophagus tissue. 14th International Conference on Advanced Computing and Communications, ADCOM 2006, pp 211–216, 2006.

  14. Kim, T. Y., Choi, H. J., Hwang, H. G., and Choi, H. K., Three-dimensional texture analysis of renal cell carcinoma cell nuclei for computerized automatic grading. J. Med. Syst. 34(4):709–716, 2010.

    Article  Google Scholar 

  15. Al-Kofahi, Y., Lassoued, W., Lee, W., and Roysam, B., Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans. Biomed. Eng. 57(4):841–852, 2010.

    Article  Google Scholar 

  16. Zhongyu X, Fen H, Hongcheng G, Quansheng D., Support vector machine image segmentation algorithm applied to angiogenesis quantification. Proceedings – 2010 6th International Conference on Natural Computation, ICNC 2010, Volume 2 pp 928–931, 2010.

  17. Wu, H., Fiel, M. I., Schiano, T. D., Ramer, M., Burstein, D., and Gil, J., Segmentation of textured cell images based on frequency analysis. IET Image Process. 5(2):148–158, 2011.

    Article  Google Scholar 

  18. Chaabane, S. B., and Fnaiech, F., Color edges extraction using statistical features and automatic threshold technique: application to the breast cancer cells. Biomed. Eng. 13:4, 2014. doi:10.1186/1475-925X-13-4.

    Google Scholar 

  19. Sagonas, C., Marras, I., Kasampalidis, I., Pitas, I., Lyroudia, K., and Karayannopoulou, G., FISH image analysis using a modified radial basis function network. Biomed. Signal Process. Control 8(1):30–40, 2013.

    Article  Google Scholar 

  20. Chen, A., David, B. H., Bissonnette, M., Scaglione-Sewell, B., and Brasitus, T. A., 1, 25-Dihysdroxyvitamin D3 stimulates activator Protein- 1 dependent Caco-2 cell differentiation. J. Biol. Chem. 274:35505–35513, 1999.

    Article  Google Scholar 

  21. Sundaram, S., Sea, A., Feldman, S., Strawbridge, R., Hoopes, P., Demidenko, E., Binderup, L., and Gewirtz, A., The combination of a potent vitamin D3 analog, EB 1089, with ionizing radiation reduces tumor growth and induces Apoptosis of MCF-7 breast tumor Xenografts in nude mice. Clin. Cancer Res. 9(6):2350–2356, 2003.

    Google Scholar 

  22. Naghibi, S., Teshnehlab, M., and Shoorehdeli, M. A., Breast cancer classification based on advanced multi dimensional fuzzy neural network. J. Med. Syst. 36(5):2713–2720, 2012.

    Article  Google Scholar 

  23. Sokouti, B., Haghipour, S., and Tabrizi, A. D., A pilot study on image analysis techniques for extracting early uterine cervix cancer cell features. J. Med. Syst. 36(3):1901–1907, 2012.

    Article  Google Scholar 

  24. Krishnan, M. M. R., Shah, P., Chakraborty, C., and Ray, A. K., Statistical analysis of textural features for improved classification of oral histopathological images. J. Med. Syst. 36(2):865–881, 2012.

    Article  Google Scholar 

  25. Cortes, C., and Vapnik, V., Support-vector networks. Mach. Learn. 20:273–297, 1995.

    MATH  Google Scholar 

  26. Friedman, N., Geiger, D., Moises, et al., Bayesian network classifiers. Mach. Learn. 29:131–163, 1997.

    Article  MATH  Google Scholar 

  27. Roussopoulos, N., Kelley, S., and Vincent, F., Nearest neighbor queries. SIGMOD Rec 24(2):71–79, 1995.

    Article  Google Scholar 

  28. Mitchell T., Decision tree learning. In T. Mitchell, Machine Learning, The McGraw-Hill Companies, Inc. 1997, pp. 52–78, 1997.

  29. Breiman, L., Random forests. Mach. Learn. 45(1):5–32, 2001.

    Article  MATH  Google Scholar 

  30. Batenburg, K. J., and Sijbers, J., Adaptive thresholding of tomograms by projection distance minimization. Pattern Recogn. 42(10):2297–2305, 2009.

    Article  MATH  Google Scholar 

  31. Ridler, T. W., and Calvard, S., Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cybern. 8:630–632, 1978.

    Article  Google Scholar 

  32. Harangi B, Qureshi RJ, Csutak A, Petö T, Hajdu A., Automatic detection of the optic disc using majority voting in a collection of optic disc detectors. 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010, pp 1329–1332. 2010.

  33. Suzuki, K., Horiba, I., and Sugie, N., Linear-time connected-component labeling based on sequential local operations. Comp. Vision Image Underst. 89(1):1–23, 2003.

    Article  MATH  Google Scholar 

  34. Goudas T, Maglogiannis I., Cancer cells detection and pathology quantification utilizing image analysis techniques. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society, Conference, pp 4418–4421. 2012.

  35. National Cancer Institute, http://web.ncifcrf.gov/

  36. Soule, H. D., Vazquez, J., Long, A., Albert, S., and Brennan, M., A human cell line from a pleural effusion derived from a breast carcinoma. J. Natl. Cancer Inst. 51(5):1409–1416, 1973.

    Google Scholar 

  37. Tasoulis, S. K., Tasoulis, D. K., and Plagianakos, V. P., Enhancing principal direction divisive clustering. Pattern Recogn. 43(10):3391–3411, 2010.

    Article  MATH  Google Scholar 

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Affiliations

  1. Department of Digital Systems, University of Piraeus, Grigoriou Lampraki 126, PC 18532, Piraeus, Greece

    Theodosios Goudas & Ilias Maglogiannis

Authors
  1. Theodosios Goudas
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  2. Ilias Maglogiannis
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Correspondence to Ilias Maglogiannis.

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This article is part of the Topical Collection on Transactional Processing Systems

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Goudas, T., Maglogiannis, I. An Advanced Image Analysis Tool for the Quantification and Characterization of Breast Cancer in Microscopy Images. J Med Syst 39, 31 (2015). https://doi.org/10.1007/s10916-015-0225-3

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  • DOI: https://doi.org/10.1007/s10916-015-0225-3

Keywords

  • Breast cancer
  • Pathology quantification
  • Microscopy
  • Image mining
  • Classification