Cancer cells detection and pathology quantification utilizing image analysis techniques.
This paper presents an advanced image analysis tool for the accurate and fast characterization and quantification of cancer and apoptotic cells in microscopy images utilizing adaptive thresholding and a Support Vector Machines classifier. The segmentation results are also enhanced through a Majority Voting and a Watershed technique. The proposed tool was evaluated by experts on breast cancer images and the reported results were accurate and reproducible.
University of Central Greece, Department of Computer Science and Biomedical Informatics, Greece. goudas@ucg.gr
Theodosios
T
Goudas
Ilias
I
Maglogiannis
D000465:Algorithms; D000818:Animals; D001943:Breast Neoplasms / Q000473:pathology*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D051379:Mice; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine