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Identification of tumor epithelium and stroma in tissue microarrays using texture analysis

Nina Linder1, Juho Konsti1, Riku Turkki12, Esa Rahtu2, Mikael Lundin1, Stig Nordling45, Caj Haglund3, Timo Ahonen26, Matti Pietikäinen2 and Johan Lundin17*

Author Affiliations

1 Institute for Molecular Medicine Finland (FIMM), P.O. Box 20, FI-00014 University of Helsinki, Helsinki, Finland

2 Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, P.O. Box 4500, FI-90014 Oulu, Finland

3 Department of General Surgery, Helsinki University Central Hospital, PO Box 340, Haartmaninkatu 4, Helsinki, 00290 HUS, Finland

4 Department of Pathology, Haartman Institute, University of Helsinki, Haartmaninkatu 3, P.O. Box 21, FI-00014 University of Helsinki, Helsinki, Finland

5 Department of Pathology, Helsinki University Central Hospital, Haartmaninkatu 3, Helsinki, FI-00014, Finland

6 Visual Computing and Ubiquitous Imaging Research Team, Nokia Research Center, Palo Alto, CA, USA

7 Division of Global Health/IHCAR, Karolinska Institutet, Nobels väg 9, SE-171 77 Stockholm, Sweden

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Diagnostic Pathology 2012, 7:22  doi:10.1186/1746-1596-7-22

Published: 2 March 2012



The aim of the study was to assess whether texture analysis is feasible for automated identification of epithelium and stroma in digitized tumor tissue microarrays (TMAs). Texture analysis based on local binary patterns (LBP) has previously been used successfully in applications such as face recognition and industrial machine vision. TMAs with tissue samples from 643 patients with colorectal cancer were digitized using a whole slide scanner and areas representing epithelium and stroma were annotated in the images. Well-defined images of epithelium (n = 41) and stroma (n = 39) were used for training a support vector machine (SVM) classifier with LBP texture features and a contrast measure C (LBP/C) as input. We optimized the classifier on a validation set (n = 576) and then assessed its performance on an independent test set of images (n = 720). Finally, the performance of the LBP/C classifier was evaluated against classifiers based on Haralick texture features and Gabor filtered images.


The proposed approach using LPB/C texture features was able to correctly differentiate epithelium from stroma according to texture: the agreement between the classifier and the human observer was 97 per cent (kappa value = 0.934, P < 0.0001) and the accuracy (area under the ROC curve) of the LBP/C classifier was 0.995 (CI95% 0.991-0.998). The accuracy of the corresponding classifiers based on Haralick features and Gabor-filter images were 0.976 and 0.981 respectively.


The method illustrates the capability of automated segmentation of epithelial and stromal tissue in TMAs based on texture features and an SVM classifier. Applications include tissue specific assessment of gene and protein expression, as well as computerized analysis of the tumor microenvironment.

Virtual slides

The virtual slide(s) for this article can be found here: webcite

Image analysis; Texture classification; Pattern recognition; Stroma; Epithelium; Local binary patterns; Haralick; Gabor; Support vector machine