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[Analysis of histological datasets by signal processing methods]. / Signaltheoretische Analyse histologischer Daten im Ortsfrequenzraum.
Weichert, F; Groh, A; Shamaa, A; Richards, T; Awd, S; Linder, R; Landes, C A; Wagner, M.
Affiliation
  • Weichert F; Fakultät für Informatik, Lehrstuhl für Graphische Systeme, Technische Universität Dortmund, Otto-Hahn-Strasse 16, 44221 Dortmund. frank.weichert@tu-dortmund.de
Pathologe ; 29 Suppl 2: 129-34, 2008 Nov.
Article in De | MEDLINE | ID: mdl-19039617
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Image Processing, Computer-Assisted / Artificial Intelligence / Cytological Techniques / Histological Techniques / Models, Theoretical Limits: Humans Language: De Journal: Pathologe Year: 2008 Type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Image Processing, Computer-Assisted / Artificial Intelligence / Cytological Techniques / Histological Techniques / Models, Theoretical Limits: Humans Language: De Journal: Pathologe Year: 2008 Type: Article