Advanced cancer cell characterization and quantification of microscopy images

Authors:
Theodosios Goudas;Ilias Maglogiannis
Affiliations:
Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, Greece;Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, Greece
Venue:
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Year:
2012

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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.