- Asif Ahmad1,
- Amina Asif1,
- Nasir Rajpoot2,
- Muhammad Arif3 &
- …
- Fayyaz ul Amir Afsar Minhas ORCID:orcid.org/0000-0001-9129-11891
911Accesses
9Citations
1Altmetric
Abstract
Nuclei detection in histology images is an essential part of computer aided diagnosis of cancers and tumors. It is a challenging task due to diverse and complicated structures of cells. In this work, we present an automated technique for detection of cellular nuclei in hematoxylin and eosin stained histopathology images. Our proposed approach is based on kernelized correlation filters. Correlation filters have been widely used in object detection and tracking applications but their strength has not been explored in the medical imaging domain up till now. Our experimental results show that the proposed scheme gives state of the art accuracy and can learn complex nuclear morphologies. Like deep learning approaches, the proposed filters do not require engineering of image features as they can operate directly on histopathology images without significant preprocessing. However, unlike deep learning methods, the large-margin correlation filters developed in this work are interpretable, computationally efficient and do not require specialized or expensive computing hardware.Availability: A cloud based webserver of the proposed method and its python implementation can be accessed at the following URL:http://faculty.pieas.edu.pk/fayyaz/software.html#corehist.
This is a preview of subscription content,log in via an institution to check access.
Access this article
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
Price includes VAT (Japan)
Instant access to the full article PDF.




Similar content being viewed by others
References
Demir, C., and Yener, B., Automated cancer diagnosis based on histopathological images: a systematic survey. In:Dept Comput Sci Rensselaer Polytech. Inst. Troy NY USA Tech Rep TR-05-09, 2005.
Dunne, B., and Going, J.J., Scoring nuclear pleomorphism in breast cancer.Histopathology. 39(3):259–265, Sep. 2001.
Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R.J., Cree, I.A., and Rajpoot, N.M., Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images.IEEE Trans. Med. Imaging. 35(5):1196–1206, 2016.
Irshad, H., Veillard, A., Roux, L., and Racoceanu, D., Methods for nuclei detection, segmentation, and classification in digital histopathology: A review-current status and future potential.IEEE Rev. Biomed. Eng. 7:97–114, 2014.
Vahadane, A., and Sethi, A., Towards generalized nuclear segmentation in histological images. In:Bioinformatics and Bioengineering (BIBE), 2013 I.E. 13th International Conference on, pp. 1–4, 2013.
Kumar, R., Srivastava, R., and Srivastava, S., Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features.J. Med. Eng. 2015:e457906, 2015.
Wang, W., Ozolek, J.A., and Rohde, G.K., Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images.Cytometry A. 9999A:NA-NA, 2010.
Chomphuwiset, P., Magee, D.R., Boyle, R.D., and Treanor, D., Context-based classification of cell nuclei and tissue regions in liver histopathology. In:MIUA, pp. 239–244, 2011.
Yuan, Y., et al., Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling.Sci. Transl. Med. 4(157):157ra143, 2012.
Jain, A., Atey, S., Vinayak, S., and Srivastava, V., Cancerous cell detection using histopathological image analysis.Int. J. Innov. Res. Comput. Commun. Eng. 2(12):7419–7426, 2015.
Cireşan, D.C., Giusti, A., Gambardella, L.M., and Schmidhuber, J., Mitosis detection in breast cancer histology images with deep neural networks. In:International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 411–418, 2013.
Mao, K.Z., Zhao, P., and Tan, P.-H., Supervised learning-based cell image segmentation for p53 immunohistochemistry.IEEE Trans. Biomed. Eng. 53(6):1153–1163, 2006.
Sommer, C., Fiaschi, L., Hamprecht, F.A., and Gerlich, D.W., Learning-based mitotic cell detection in histopathological images. In:Pattern Recognition (ICPR), 2012 21st International Conference on, pp. 2306–2309, 2012.
Veillard, A., Kulikova, M.S., and Racoceanu, D., Cell nuclei extraction from breast cancer histopathologyimages using colour, texture, scale and shape information.Diagn. Pathol. 8(Suppl 1):S5, 2013.
Vink, J.p., Van Leeuwen, M.b., Van Deurzen, C.h.m., and De Haan, G., Efficient nucleus detector in histopathology images.J. Microsc. 249(2):124–135, 2013.
Madabhushi, A., and Lee, G., Image analysis and machine learning in digital pathology: Challenges and opportunities.Med. Image Anal. 33:170–175, 2016.
Xie, Y., Xing, F., Kong, X., Su, H., and Yang, L., Beyond classification: Structured regression for robust cell detection using convolutional neural network. In: Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (Eds.),Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer International Publishing, pp. 358–365, 2015.
Xu, J., et al., Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images.IEEE Trans. Med. Imaging. 35(1):119–130, 2016.
Ali, S., Veltri, R., Epstein, J.I., Christudass, C., and Madabhushi, A., Adaptive energy selective active contour with shape priors for nuclear segmentation and gleason grading of prostate cancer. In:International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 661–669, 2011.
Ali, S., and Madabhushi, A., An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery.IEEE Trans. Med. Imaging. 31(7):1448–1460, Jul. 2012.
Al-Kofahi, Y., Lassoued, W., Lee, W., and Roysam, B., Improved automatic detection and segmentation of cell nuclei in histopathology images.IEEE Trans. Biomed. Eng. 57(4):841–852, Apr. 2010.
Jung, C., Kim, C., Chae, S.W., and Sukjoong, O., Unsupervised segmentation of overlapped nuclei using Bayesian classification.IEEE Trans. Biomed. Eng. 57(12):2825–2832, Dec. 2010.
Di Cataldo, S., Ficarra, E., Acquaviva, A., and Macii, E., Automated segmentation of tissue images for computerized IHC analysis.Comput. Methods Programs Biomed. 100(1):1–15, Oct. 2010.
Huang, P.-W., and Lai, Y.-H., Effective segmentation and classification for HCC biopsy images.Pattern Recognit. 43(4):1550–1563, Apr. 2010.
Kong, H., Gurcan, M., and Belkacem-Boussaid, K., Partitioning histopathological images: An integrated framework for supervised color-texture segmentation and cell splitting.IEEE Trans. Med. Imaging. 30(9):1661–1677, Sep. 2011.
Mouelhi, A., Sayadi, M., and Fnaiech, F., c. In:Communications, Computing and Control Applications (CCCA), 2011 International Conference on, pp. 1–6, 2011.
Wienert, S., et al., Detection and segmentation of cell nuclei in virtual microscopy images: A minimum-model approach.Sci. Rep. 2:503, 2012.
Bolme, D.S., Beveridge, J.R., Draper, B.A., and Lui, Y.M., Visual object tracking using adaptive correlation filters. In:Computer Vision and Pattern Recognition (CVPR), 2010 I.E. Conference on, pp. 2544–2550, 2010.
Bolme, D.S., Draper, B.A., and Beveridge, J.R., Average of synthetic exact filters. In:Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 2105–2112, 2009.
Henriques, J.F., Caseiro, R., Martins, P., and Batista, J., High-speed tracking with kernelized correlation filters.Pattern Anal. Mach. Intell. IEEE Trans. On. 37(3):583–596, 2015.
Boddeti, V. N., Kanade, T., and Kumar, B. V. K. V., Correlation Filters for Object Alignment. 2013, pp. 2291–2298.
Danelljan, M., Hager, G., Shahbaz Khan, F., and Felsberg, M., Learning spatially regularized correlation filters for visual tracking. In:Proceedings of the IEEE International Conference on Computer Vision, pp. 4310–4318, 2015.
Savvides, M., Kumar, B. V., and Khosla, P., Face verification using correlation filters.3rd IEEE Autom. Identif. Adv. Technol., 56–61, 2002.
Savvides, M. and Kumar, B. V. K. V., Efficient design of advanced correlation filters for robust distortion-tolerant face recognition. In:IEEE Conference on Advanced Video and Signal Based Surveillance, 2003. Proceedings, 2003, pp. 45–52.
Proakis, J.G., and Manolakis, D.K.,Digital Signal Processing, 4th edn. N.J: Pearson, Upper Saddle River, 2006.
Schölkopf, B., Herbrich, R., and Smola, A.J., A generalized representer theorem. In: Helmbold, D., and Williamson, B. (Eds.),Computational Learning Theory. Springer, Berlin Heidelberg, pp. 416–426, 2001.
Schölkopf, B. and Smola, A. J.,Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.
Hofmann, M., Support vector machines—Kernels and the kernel trick.Notes. 26, 2006.
Jäkel, F., Schölkopf, B., and Wichmann, F.A., A tutorial on kernel methods for categorization.J. Math. Psychol. 51(6):343–358, 2007.
Shawe-Taylor, J., and Cristianini, N.,Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
Schölkopf, B., and Burges, C.J.C.,Advances in Kernel Methods: Support Vector Learning. MIT Press, 1999.
Rao, K.R., Kim, D.N., and Hwang, J.-J.,Fast Fourier Transform - Algorithms and Applications, 1st edn. Springer Publishing Company, Incorporated, 2010.
Bankman, I.N.,Handbook of Medical Imaging: Processing and Analysis Management. Academic Press, 2000.
Chitode, J.S.,Digital Signal Processing. Technical Publications, 2009.
Ruifrok, A.C., and Johnston, D.A., Quantification of histochemical staining by color deconvolution.Anal. Quant. Cytol. Histol. Int. Acad. Cytol. Am. Soc. Cytol. 23(4):291–299, 2001.
Harris, and Fredric, J., On the use of windows for harmonic analysis with the discrete Fourier transform.Proc. IEEE:51–83, 1978.
Alpaydin, E.,Introduction to Machine Learning. MIT Press, 2014.
Kuse, M., Kalasannavar, V., Rajpoot, N., Wang, Y.-F., and Khan, M., Local isotropic phase symmetry measure for detection of beta cells and lymphocytes.J. Pathol. Inform. 2(2):2, 2011.
Funding
Asif Ahmed is funded by a fellowship from the Pakistan Institute of Engineering and Applied Sciences. Amina Asif acknowledges the funding support from the IT and Telecom Endowment Fund at Pakistan Institute of Engineering and Applied Sciences.
Author information
Authors and Affiliations
Biomedical Informatics Research Laboratory, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, PO Nilore, Islamabad, Pakistan
Asif Ahmad, Amina Asif & Fayyaz ul Amir Afsar Minhas
Department of Computer Science, University of Warwick, Coventry, UK
Nasir Rajpoot
Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, PO Nilore, Islamabad, Pakistan
Muhammad Arif
- Asif Ahmad
You can also search for this author inPubMed Google Scholar
- Amina Asif
You can also search for this author inPubMed Google Scholar
- Nasir Rajpoot
You can also search for this author inPubMed Google Scholar
- Muhammad Arif
You can also search for this author inPubMed Google Scholar
- Fayyaz ul Amir Afsar Minhas
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toAsif Ahmad.
Ethics declarations
Conflict of Interest
The authors declare no conflict of interest.
Ethical Approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
This article is part of the Topical Collection onImage & Signal Processing
Rights and permissions
About this article
Cite this article
Ahmad, A., Asif, A., Rajpoot, N.et al. Correlation Filters for Detection of Cellular Nuclei in Histopathology Images.J Med Syst42, 7 (2018). https://doi.org/10.1007/s10916-017-0863-8
Received:
Accepted:
Published:
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative