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Title:

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

Document type:
Journal Article
Author(s):
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...     »
Journal title abbreviation:
Conf Proc IEEE Eng Med Biol Soc
Year:
2010
Journal volume:
1
Pages contribution:
49-52
Language:
eng
Fulltext / DOI:
doi:10.1109/IEMBS.2010.5626299
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/21095879
Print-ISSN:
1557-170X
TUM Institution:
Institut für Allgemeine Pathologie und Pathologische Anatomie
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