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Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA, USA and Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
Dept. of Electronics and Communication Engineering (ECE), National Institute of Technology (NIT), Tiruchirappalli, India.
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2012 (English)In: Histopathology Image Analysis (HIMA): a MICCAI 2012 workshop, 2012Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

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 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.

Methods and Material:

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 WND-CHARM-inspired features. The extracted features are used into two different multivariate classifiers (SVM and 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:

Good results have been obtained by using the combinations of GLCM and wavelets and texture features, edge features, histograms, transforms, etc. (WND-CHARM). 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 tumour grading.

Place, publisher, year, edition, pages
2012.
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-188447OAI: oai:DiVA.org:uu-188447DiVA: diva2:577872
Conference
Histopathology Image Analysis (HIMA), a MICCAI 2012 workshop, 5 October, 2012, Nice, France
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2013-07-05Bibliographically approved

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http://www2.warwick.ac.uk/fac/sci/dcs/events/hima2012/

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Issac Niwas, SwamidossKårsnäs, AndreasKampf, CarolineSimonsson, MartinWählby, CarolinaStrand, Robin

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Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer InteractionMolecular and Morphological PathologyScience for Life Laboratory, SciLifeLab
Medical Image Processing

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