title: Robust automatic coregistration, segmentation, and classification of cell nuclei in multimodal cytopathological microscopic images.
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 repeated staining of a cell smear. The correlation of features and data extracted from the different stains, and related to relocated individual cells, may yield a dramatic increase of diagnostic reliability. In order to utilise the technique, fully automatic, adaptive image preprocessing techniques need to be applied, which are described in this article: coregistration of multimodal images, segmentation, and classification of cell nuclei. The presented feasibility study shows both efficiency and robustness of all steps being high regarding medical image material, and it strongly supports clinical application.
authors.affiliation: Institute for Measurement Techniques and Image Processing, Aachen University, Templergraben 55, Aachen 52056, 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: D002467:Cell Nucleus / Q000648:ultrastructure; D003581:Cytodiagnosis; D005240:Feasibility Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008853:Microscopy / Q000379:methods; D009369:Neoplasms / Q000175:diagnosis / Q000473:pathology* / Q000648:ultrastructure; D010363:Pattern Recognition, Automated; D013194:Staining and Labeling