title: Leukocyte segmentation and classification in blood-smear images.
abstract: The detection and classification of leukocytes in blood smear images is a routine task in medical diagnosis. In this paper we present a fully automated approach to leukocyte segmentation that is robust with respect to cell appearance and image quality. A set of features is used to describe cytoplasm and nucleus properties. Pairwise SVM classification is used to discriminate between different cell types. Evaluation on a set of 1166 images (13 classes) resulted in 95% correct segmentations and 75% to 99% correct classification (with reject option).
authors.affiliation: Advanced Computer Vision GmbH - ACV, Donau-City-Strasse 1, 1220 Wien, Austria, Email: herbert.ramoser@acv.ac.at.
authors.forename: Herbert
authors.initials: H
authors.lastname: Ramoser
authors.forename: Vincent
authors.initials: V
authors.lastname: Laurain
authors.forename: Horst
authors.initials: H
authors.lastname: Bischof
authors.forename: Rupert
authors.initials: R
authors.lastname: Ecker