Journal of Pathology Informatics Journal of Pathology Informatics
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SYMPOSIUM - ORIGINAL RESEARCH
Year : 2013  |  Volume : 4  |  Issue : 2  |  Page : 14

Automated classification of immunostaining patterns in breast tissue from the human protein Atlas


1 Department of Electronics and Communication Engineering, National Institute of Technology (NIT), Tiruchirappalli, Tamil Nadu, India; Centre for Image Analysis (CBA) and SciLife Lab, Uppsala University, Uppsala, Sweden
2 Centre for Image Analysis (CBA) and SciLife Lab, Uppsala University, Uppsala, Sweden; Visiopharm A/S, Hørsholm, Denmark
3 Imaging Platform, Broad Institute of Harvard and Massachusetts Institute Technology (MIT), Cambridge, Massachusetts, MA, USA; Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
4 Department of Electronics and Communication Engineering, National Institute of Technology (NIT), Tiruchirappalli, Tamil Nadu, India
5 Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
6 Centre for Image Analysis (CBA) and SciLife Lab, Uppsala University, Uppsala, Sweden
7 Centre for Image Analysis (CBA) and SciLife Lab, Uppsala University, Uppsala, Sweden; Imaging Platform, Broad Institute of Harvard and Massachusetts Institute Technology (MIT), Cambridge, Massachusetts, MA, USA

Correspondence Address:
Issac Niwas Swamidoss
Department of Electronics and Communication Engineering, National Institute of Technology (NIT), Tiruchirappalli, Tamil Nadu, India; Centre for Image Analysis (CBA) and SciLife Lab, Uppsala University, Uppsala, Sweden

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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.109881

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Background: The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. Materials and Methods: The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. Results: We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Conclusions: Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.


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