Segmentation of histopathology HER2/neu images with fuzzy decision tree and Takagi-Sugeno reasoning.
The Human Epidermal Growth Factor Receptor 2 (HER2/neu) is a biomarker, recognized as a valuable prognostic and predictive factor for breast cancer. In approximately 20% of primary breast cancers, the HER2/neu protein is over-expressed. By recent clinical research, a treatment procedure, with corresponding monoclonal antibodies specifically designed to target the HER2/neu receptor, was confirmed. Therefore, in modern breast cancer diagnostics, it is critical to provide accurate recognition of the HER2/neu positive breast cancer. This can be done by segmentation of the membranes of cancer cells that are visualized as HER2/neu over-expressed on images acquired from corresponding histopathology preparations. In our research, we propose an accurate segmentation process of these structures using an appropriately defined fuzzy decision tree. Moreover, we introduce a new reasoning concept based on the Takagi-Sugeno inference model.
Institute of Informatics, Wroclaw University of Technology, Poland. Electronic address: martin.tabakow@pwr.wroc.pl.
Martin
M
Tabakov
Institute of Informatics, Wroclaw University of Technology, Poland. Electronic address: 183610@student.pwr.wroc.pl.
Pawel
P
Kozak
D001943:Breast Neoplasms / Q000737:chemistry*; D018270:Carcinoma, Ductal, Breast / Q000737:chemistry; D003663:Decision Trees; D005260:Female; D017143:Fuzzy Logic; D006651:Histocytochemistry / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000032:analysis / Q000737:chemistry*
D018719:Erb-b2 Receptor Tyrosine Kinases; C508053:ERBB2 protein, human
Fuzzy decision tree; HER2/neu breast cancer; Histopathology image processing; Image segmentation; Takagi–Sugeno inference