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Diagnostic Pathology
Published in: Diagnostic Pathology 1/2012

Open Access 01-12-2012 | Research

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

Authors: Nina Linder, Juho Konsti, Riku Turkki, Esa Rahtu, Mikael Lundin, Stig Nordling, Caj Haglund, Timo Ahonen, Matti Pietikäinen, Johan Lundin

Published in: Diagnostic Pathology | Issue 1/2012

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Abstract

Background

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.

Results

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.

Conclusions

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: http://​www.​diagnosticpathol​ogy.​diagnomx.​eu/​vs/​4123422336534537​
Appendix
Available only for authorised users
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Metadata
Title
Identification of tumor epithelium and stroma in tissue microarrays using texture analysis
Authors
Nina Linder
Juho Konsti
Riku Turkki
Esa Rahtu
Mikael Lundin
Stig Nordling
Caj Haglund
Timo Ahonen
Matti Pietikäinen
Johan Lundin
Publication date
01-12-2012
Publisher
BioMed Central
Published in
Diagnostic Pathology / Issue 1/2012
Electronic ISSN: 1746-1596
DOI
https://doi.org/10.1186/1746-1596-7-22

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