Abstract
We present an image analysis pipeline for identifying cells in histopathology images of cancer. The analysis starts with segmentation using multi-phase level sets, which is insensitive to initialization and enables automatic detection of arbitrary objects. Morphological operations are used to remove small spots in the segmented images. The target cells are then identified based on their features. The detected cells were compared with the manual detection performed by pathologists. The quantitative evaluation shows promise and utility of our technique.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fatakdawala, H., Xu, J., Basavanhally, A., Bhanot, G., Ganesan, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Expectation maximization driven geodesic active contour with overlap resolution (emagacor): Application to lymphocyte segmentation on breast cancer histopathology. IEEE Trans. Biomedical Engineering 99, 1–8 (2010)
A. C. Society: Breast cancer facts and figures. American Cancer Society, Inc., Atlanta (2009-2010)
Alexe, G., Dalgin, G.S., Scanfeld, D., Tamayo, P., Mesirov, J.P., DeLisi, C., Harris, L., Barnard, N., Martel, M., Levine, A.J., Ganesan, S., Bhanot, G.: High expression of lymphocyte-associated genes in node-negative her2+ breast cancers correlates with lower recurrence rates. Cancer Research 67, 10669–10676 (2007)
Griffin, N.R., Howard, M.R., Quirke, P., O’Brien, C.J., Child, J.A., Bird, C.C.: Prognostic indicators in centroblastic-centrocytic lymphoma. Journal of Clinical Pathology 41, 866–870 (1988)
The non-hodgkin’s lymphoma classification project, a clinical evaluation of the international lymphoma study group classification of non-hodgkin’s lymphoma. Blood, 3909–3918 (1997)
Friedberg, J.: Treatment of follicular non-hodgkin’s lymphoma: the old and the new. Semin Hematology 2, s2–s6 (2008)
Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the mumford and shah model. International Journal of Computer Vision 50(3), 271–293 (2002)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Processing 10(2), 266–277 (2001)
Meyer, F.: Topographic distance and watershed lines. Signal Processing 38(1), 113–125 (1994)
Cheng, J., Rajapakse, J.C.: Segmentation of clustered nuclei with shape markers and marking function. IEEE Trans. Biomedical Engineering 56(3), 741–748 (2009)
Huang, K., Murphy, R.F.: Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinformatics 5(78) (2004)
Mundra, P.A., Rajapakse, J.C.: SVM-RFE with MRMR filter for gene selection. IEEE Transactions on NanoBioscience 9(1), 31–37 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheng, J., Veronika, M., Rajapakse, J.C. (2010). Identifying Cells in Histopathological Images. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds) Recognizing Patterns in Signals, Speech, Images and Videos. ICPR 2010. Lecture Notes in Computer Science, vol 6388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17711-8_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-17711-8_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17710-1
Online ISBN: 978-3-642-17711-8
eBook Packages: Computer ScienceComputer Science (R0)