title: Classification of apoptosis using advanced clustering techniques on digital microscopic images.
abstract: Programmed cell death, also known as apoptosis is of fundamental importance in many biological processes and also highly associated with serious diseases like cancer and HIV. The current paper presents an innovative method for apoptosis phenomenon characterization based on apoptotic cell quantification and detection using active contours. Subsequently, we employ appropriate data mining techniques and perform characterization of apoptosis on digital microscopic images. A particular class of clustering algorithms, utilizing information driven by the Principal Component Analysis, has been very successful in dealing with such data. In this work, we employ a recently proposed clustering algorithm to solve this real world clustering task.
authors.affiliation: Department of Computer Science and Biomedical Informatics, University of Central Greece, Papassiopoulou 2-4, Lamia, 35100, Greece. stas@uc.gr
authors.forename: S K
authors.initials: SK
authors.lastname: Tasoulis
authors.forename: C N
authors.initials: CN
authors.lastname: Doukas
authors.forename: I
authors.initials: I
authors.lastname: Maglogiannis
authors.forename: V P
authors.initials: VP
authors.lastname: Plagianakos
mesh_terms: D000465:Algorithms; D017209:Apoptosis; D016000:Cluster Analysis; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008853:Microscopy / Q000379:methods*