Abstract
In this paper we present an advanced image analysis tool for the accurate characterization and quantification of cancer and apoptotic cells in microscopy images. Adaptive thresholding and Support Vector Machines classifiers were utilized for this purpose. The segmentation results are improved through the application of morphological operators such as Majority Voting and a Watershed technique. The proposed tool was evaluated on breast cancer images by medical experts and the results were accurate and reproducible.
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German, R.R., Fink, A.K., Heron, M., Johnson, C.J., Finch, J.L., 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)
Loncaster, J., Dodwell, D.: Adjuvant radiotherapy in breastcancer. Are there factors that allow selection of patients who do notrequire adjuvant radiotherapy following breast-conserving surgery forbreast cancer? Minerva Med. 93, 101–107 (2002)
Chen, A., David, B.H., Bissonnette, M., Scaglione-Sewell, B., Brasitus, T.A.: 1, 25-Dihysdroxyvitamin D3 stimulates activator Protein- 1 dependent Caco-2 cell differentiation. J. Biol. Chem. 274, 35505–35513 (1999)
Hansen, C.M., Hamberg, K.J., Binderup, E., Binderup, L.: Seocalcitol (EB 1089): A vitamin D analogue of anticancer potential. Background, design, synthesis, preclinical and clinical evaluation. Curr. Pharm. Design 6, 803–828 (2000)
Loukas, C.G., Wilson, G.D., Vojnovic, B., 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)
Saveliev, P., Pahwa, A.: A topological approach to cell counting. In: Proceedings of Workshop on Bio-Image Informatics: Biological Imaging, Computer Vision and Data Mining, Center for Bio-Image Informatics, University of California - Santa Barbara, USA, January 17-18 (2008)
Phukpattaranont, P., Boonyaphiphat, P.: Colour based segmentation of nuclear stained breast cancer cell images. ECTI Transactions on Electrical Eng. Electronics and Communications 5(2), 158–164 (2007)
Maglogiannis, I., Sarimveis, H., Kiranoudis, C., Chatzioannou, H., Oikonomou, N., Aidinis, V.: Radial Basis Function neural networks classification for the recognition of idiopathic pulmonary fibrosis in microscopic images. IEEE Transactions on Information Technology in Biomedicine 12(1), 42–54 (2008)
Tosun, A.B., Gunduz-Demir, C.: Graph Run-Length Matrices for Histopathological Image Segmentation. IEEE Transactions on Medical Imaging 30(3), 721–732 (2011)
Soule, H.D., Vazquez, J., Long, A., Albert, S., Brennan, M.: A human cell line from a pleural effusion derived from a breast carcinoma. Journal of the National Cancer Institute 51(5), 1409–1416 (1973)
Abramoff, M.D., Magalhaes, P.J., Ram, S.J.: Image Processing with Image. Biophotonics International 11(7), 36–42 (2004)
Batenburg, K.J., Sijbers, J.: Adaptive thresholding of tomograms by projection distance minimization. Pattern Recognition 42(10), 2297–2305 (2009)
Ridler, T.W., Calvard, S.: Picture thresholding using an iterative selection method. IEEE Trans. System, Man and Cybernetics, SMC 8, 630–632 (1978)
Harangi, B., Qureshi, R.J., Csutak, A., Petö, T., Hajdu, A.: Automatic detection of the optic disc using majority voting in a collection of optic disc detectors. In: Proceedings of ISBI 2010, pp. 1329–1332 (2010)
Sundaram, S., Sea, A., Feldman, S., Strawbridge, R., Hoopes, P., Demidenko, E., Binderup, L., 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 TumorXenografts in Nude Mice1. Clinical Cancer Research 9, 2350–2356 (2003)
Suzuki, K., Horiba, I., Sugie, N.: Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding 89(1), 1–23 (2003)
National Cancer Institute, http://web.ncifcrf.gov/
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Goudas, T., Maglogiannis, I. (2012). Advanced Cancer Cell Characterization and Quantification of Microscopy Images. In: Maglogiannis, I., Plagianakos, V., Vlahavas, I. (eds) Artificial Intelligence: Theories and Applications. SETN 2012. Lecture Notes in Computer Science(), vol 7297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30448-4_40
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DOI: https://doi.org/10.1007/978-3-642-30448-4_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30447-7
Online ISBN: 978-3-642-30448-4
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