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Titel:

Learning cellular texture features in microscopic cancer cell images for automated cell-detection.

Dokumenttyp:
Journal Article
Autor(en):
Kazmar, T; Smid, M; Fuchs, M; Luber, B; Mattes, J
Abstract:
In this paper we present a new approach for automated cell detection in single frames of 2D microscopic phase contrast images of cancer cells which is based on learning cellular texture features. The main challenge addressed in this paper is to deal with clusters of cells where each cell has a rather complex appearance composed of sub-regions with different texture features. Our approach works on two different levels of abstraction. First, we apply statistical learning to learn 6 different types...     »
Zeitschriftentitel:
Conf Proc IEEE Eng Med Biol Soc
Jahr:
2010
Band / Volume:
1
Seitenangaben Beitrag:
49-52
Sprache:
eng
Volltext / DOI:
doi:10.1109/IEMBS.2010.5626299
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/21095879
Print-ISSN:
1557-170X
TUM Einrichtung:
Institut für Allgemeine Pathologie und Pathologische Anatomie
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