Raquel Pezoa, Luis Salinas, Claudio Torres, Steffen Hartel, Cristian Maureira-Fredes & Paola Arce
Journal of Physics: Conference Series 762 (2016) 012050 / doi:10.1088/1742-6596/762/1/012050

Breast cancer is one of the most common cancers in women worldwide. Patient therapy is widely supported by analysis of immunohistochemically (IHC) stained tissue sections. In particular, the analysis of HER2 overexpression by immunohistochemistry helps to determine when patients are suitable to HER2-targeted treatment. Computational HER2 overexpression analysis is still an open problem and a challenging task principally because of the variability of immunohistochemistry tissue samples and the subjectivity of the specialists to assess the samples. In addition, the immunohistochemistry process can produce diverse artifacts that dicult the HER2 overexpression assessment. In this paper we study the segmentation of HER2 overexpression in IHC stained breast cancer tissue images using a support vector machine (SVM) classi er. We asses the SVM performance using diverse color and texture pixel-level features including the RGB, CMYK, HSV, CIE Lab* color spaces, color deconvolution lter and Haralick features. We measure classi cation performance for three datasets containing a total of 153 IHC images that were previously labeled by a pathologist.