A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images

Harshita Sharma, Norman Zerbe, Daniel Heim, Stephan Wienert, Hans-Michael Behrens, Olaf Hellwich, Peter Hufnagl

2015

Abstract

This paper describes a multi-resolution technique to combine diagnostically important visual information at different magnifications in H&E whole slide images (WSI) of gastric cancer. The primary goal is to improve the results of nuclei segmentation method for heterogeneous histopathological datasets with variations in stain intensity and malignancy levels. A minimum-model nuclei segmentation method is first applied to tissue images at multiple resolutions, and a comparative evaluation is performed. A comprehensive set of 31 nuclei features based on color, texture and morphology are derived from the nuclei segments. AdaBoost classification method is used to classify these segments into a set of pre-defined classes. Two classification approaches are evaluated for this purpose. A relevance score is assigned to each class and a combined segmentation result is obtained consisting of objects with higher visual significance from individual magnifications, thereby preserving both coarse and fine details in the image. Quantitative and visual assessment of combination results shows that they contain comprehensive and diagnostically more relevant information than in constituent magnifications.

References

  1. Amidror, I. (2002). Scattered data interpolation methods for electronic imaging systems: a survey. J. Electronic Imaging, 11(2):157-176.
  2. Bancroft, J. D. and Gamble, M. (2008). Theory and practice of histological techniques. Elsevier Health Sciences.
  3. Bartels, P., Thompson, D., Bibbo, M., and Weber, J. (1992). Bayesian belief networks in quantitative histopathology. Anal Quant Cytol Histol, 14(6):459-73.
  4. Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.
  5. Cabebe, E. C. and Mehta, V. K. (2008). Gastric cancer. http://emedicine.medscape.com/article/278744- overview#showall.
  6. Chen, Y. W. and Lin, C. J. (2006). Combining SVMs with various feature selection strategies. In Feature extraction, pages 315-324. Springer.
  7. Chua, T. and Merrett, N. (2012). Clinicopathologic factors associated with HER2-positive gastric cancer and its impact on survival outcomes-a systematic review. Int J Cancer, 130(12):2845-56.
  8. Diamond, J., Anderson, N., Bartels, P., Montironi, R., and Hamilton, P. (2004). The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia. Human Pathology, 35(9):1121-1131.
  9. Hamilton, P., Anderson, N., Bartels, P., and Thompson, D. (1994). Expert system support using bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of the breast. J Clin Pathol, 47(4):329-36.
  10. Haralick, R. M., Shanmugam, K. S., and Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3(6):610-621.
  11. Hufnagl, P., Schlosser, A., and Voss, K. (1984). Merkmale der Form, Grö be und Lage digitaler objekte. Bild und Ton., 37:293-298.
  12. Kong, J., Sertel, O., Shimada, H., Boyer, K. L., Saltz, J. H., and Gurcan, M. N. (2009). Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation. Pattern Recogn., 42(6):1080-1092.
  13. Kotsiantis, S. B., Zaharakis, I., and Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. In Emerging Artificial Intelligence Applications in Computer Engineering, pages 3-24. IOS Press.
  14. Nordqvist, C. (2013). What is Stomach Cancer? What is Gastric Cancer? Medical News Today. MediLexicon, Intl. http:// www.medicalnewstoday.com/articles/257341.php.
  15. Ramesh, N., Dangott, B., Salama, M. E., and Tasdizen, T. (2012). Isolation and two-step classification of normal white blood cells in peripheral blood smears. Journal of pathology informatics, 3.
  16. Rani, S., Kannammal, A., Nirmal, M., Prabhu, K., and Kumar, R. (2010). Multi-feature prostate cancer diagnosis of histological images using advanced image segmentation. IJMEI, 2(4):408-416.
  17. Roula, M., Diamond, J., Bouridane, A., Miller, P., and Amira, A. (2002). A multispectral computer vision system for automatic grading of prostatic neoplasia. In Proceedings IEEE International Symposium on Biomedical Imaging, pages 193-196.
  18. Shuttleworth, J., Todman, A., Naguib, R., Newman, B., and Bennett, M. (2002a). Colour texture analysis using cooccurrence matrices for classification of colon cancer images. In IEEE Canadian Conference on Electrical and Computer Engineering, volume 2, pages 1134- 1139.
  19. Shuttleworth, J., Todman, A., Naguib, R., Newman, B., and Bennett, M. (2002b). Multiresolution colour texture analysis for classifying colon cancer images. In Medicine and Biology, 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES, Proceedings of the Second Joint, volume 2, pages 1118,1119.
  20. VMscope GmbH (2010). Vmscope http://vmscope.com/produkte.html.
  21. Warneke, V. S., Behrens, H., B öger, C., Becker, T., Lordick, F., Ebert, M., and Röcken, C. (2013). Her2/neu testing in gastric cancer: evaluating the risk of sampling errors. Annals of Oncology, 24(3):725-733.
  22. Weind, K., Maier, C., Rutt, B., and Moussa, M. (1998). Invasive carcinomas and fibroadenomas of the breast: comparison of microvessel distributions-implications for imaging modalities. Radiology, 208(2):477-83.
  23. Wienert, S., Heim, D., Kotani, M., Lindequist, B., Stenzinger, A., Ishii, M., Hufnagl, P., Beil, M., Dietel, M., Denkert, C., and Klauschen, F. (2013). Cognitionmaster: an object-based image analysis framework. Diagnostic Pathology, 8(1):1-8.
  24. Wienert, S., Heim, D., Saeger, K., Stenzinger, A., Beil, M., Hufnagl, P., Dietel, M., Denkert, C., and Klauschen, F. (2012). Detection and segmentation of cell nuclei in virtual microscopy images: A minimum-model approach. Scientific Reports, 2.
  25. Zerbe, N. (2008). Analyse serieller histologischer Schnitte im Hinblick auf die automatische Bestimmung gleichartiger Partikel benachbarter Schnittstufen. Diplomarbeit, Fachhochschule fü r Technik und Wirtschaft Berlin.
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Paper Citation


in Harvard Style

Sharma H., Zerbe N., Heim D., Wienert S., Behrens H., Hellwich O. and Hufnagl P. (2015). A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 37-46. DOI: 10.5220/0005247900370046


in Bibtex Style

@conference{visapp15,
author={Harshita Sharma and Norman Zerbe and Daniel Heim and Stephan Wienert and Hans-Michael Behrens and Olaf Hellwich and Peter Hufnagl},
title={A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={37-46},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005247900370046},
isbn={978-989-758-091-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images
SN - 978-989-758-091-8
AU - Sharma H.
AU - Zerbe N.
AU - Heim D.
AU - Wienert S.
AU - Behrens H.
AU - Hellwich O.
AU - Hufnagl P.
PY - 2015
SP - 37
EP - 46
DO - 10.5220/0005247900370046