title: [Analysis of histological datasets by signal processing methods].
abstract: In the present study, a semi-automatic segmentation and classification algorithm is proposed for the analysis of histological and cytological images. In view of the fact that histological and cytological images usually exhibit poor contrast and blurred outlines, classical segmentation algorithms often fail to detect relevant structures. A new algorithm for texture segmentation based on signal processing methods in combination with machine learning techniques was therefore developed.
authors.affiliation: Fakultät für Informatik, Lehrstuhl für Graphische Systeme, Technische Universität Dortmund, Otto-Hahn-Strasse 16, 44221 Dortmund. frank.weichert@tu-dortmund.de
authors.forename: F
authors.initials: F
authors.lastname: Weichert
authors.forename: A
authors.initials: A
authors.lastname: Groh
authors.forename: A
authors.initials: A
authors.lastname: Shamaa
authors.forename: T
authors.initials: T
authors.lastname: Richards
authors.forename: S
authors.initials: S
authors.lastname: Awd
authors.forename: R
authors.initials: R
authors.lastname: Linder
authors.forename: C A
authors.initials: CA
authors.lastname: Landes
authors.forename: M
authors.initials: M
authors.lastname: Wagner
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003238:Connective Tissue / Q000473:pathology; D003331:Coronary Vessels / Q000473:pathology; D003584:Cytological Techniques; D006652:Histological Techniques; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D017539:Tunica Intima / Q000473:pathology; D017540:Tunica Media / Q000473:pathology