title: Automatic coregistration, segmentation and classification for multimodal cytopathology.
abstract: The paper describes the key component of the Multimodal Cell Analysis approach, a novel cytologic evaluation method for early cancer detection. The approach is based on a repeated staining of a cell smear. The correlation of features and data extracted from different stainings, and related to relocated individual cells, will yield a dramatic increase of diagnostic reliability. The necessary fully automatic preprocessing steps are presented: coregistration of multimodal images, segmentation, and classification of cell nuclei. Both efficiency and robustness of all steps reached at the current stage of research, are high regarding medical image material, and strongly support clinical application.
authors.affiliation: LfM Institute for Measurement Techniques and Image Processing, RWTH University of Technology, Aachen, D-52056 Aachen, Germany. Wuerflinger@LfM.RWTH-Aachen.De
authors.forename: Thomas
authors.initials: T
authors.lastname: Würflinger
authors.forename: Jens
authors.initials: J
authors.lastname: Stockhausen
authors.forename: Dietrich
authors.initials: D
authors.lastname: Meyer-Ebrecht
authors.forename: Alfred
authors.initials: A
authors.lastname: Böcking
mesh_terms: D001322:Autoanalysis; D014408:Biomarkers, Tumor; D002467:Cell Nucleus / Q000276:immunology; D003593:Cytoplasm / Q000276:immunology; D042241:Early Diagnosis; D005858:Germany; D006801:Humans; D008853:Microscopy / Q000379:methods*; D009369:Neoplasms / Q000175:diagnosis* / Q000648:ultrastructure; D013194:Staining and Labeling
chemical_list: D014408:Biomarkers, Tumor