Abstract
Colorectal cancer is a major cause of mortality. As the disease progresses, adenomas and their surrounding tissue are modified. Therefore, a large number of samples from the epithelial cell layer and stroma must be collected and analyzed manually to estimate the potential evolution and stage of the disease. In this study, we propose a novel method for automatic classification of tumor epithelium and stroma in digitized tissue microarrays. To this end, we use discrete Tchebichef moments (DTMs) to characterize tumors based on their textural information. DTMs are able to capture image features in a non-redundant way providing a unique description. A support vector machine was trained to classify a dataset composed of 1376 tissue microarrays from 643 patients with colorectal cancer. The proposal achieved 97.62 % of sensitivity and 95 % of specificity showing the effectiveness of the methodology.
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Acknowledgments
The authors extend their gratitude to Prof. Dr. Johan Lundin for providing the images. This publication was supported by the European social fund within the project CZ.1.07/2.3.00/30.0034 and UNAM PAPIIT grant IG100814. R. Nava thanks Consejo Nacional de Ciencia y Tecnología (CONACYT). G. González thanks CONACYT–263921 scholarship. J. Kybic was supported by the Czech Science Foundation project 14-21421S.
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Nava, R., González, G., Kybic, J., Escalante-Ramírez, B. (2016). Classification of Tumor Epithelium and Stroma in Colorectal Cancer Based on Discrete Tchebichef Moments. In: Oyarzun Laura, C., et al. Clinical Image-Based Procedures. Translational Research in Medical Imaging. CLIP 2015. Lecture Notes in Computer Science(), vol 9401. Springer, Cham. https://doi.org/10.1007/978-3-319-31808-0_10
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