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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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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