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Correlation Filters for Detection of Cellular Nuclei in Histopathology Images

  • Image & Signal Processing
  • Published:
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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.

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References

  1. 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.

    Google Scholar 

  2. Dunne, B., and Going, J.J., Scoring nuclear pleomorphism in breast cancer. Histopathology. 39(3):259–265, Sep. 2001.

    Article  CAS  PubMed  Google Scholar 

  3. 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.

    Article  PubMed  Google Scholar 

  4. 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.

    Article  PubMed  Google Scholar 

  5. 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.

    Google Scholar 

  6. 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.

    Article  Google Scholar 

  7. 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.

    Article  Google Scholar 

  8. 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.

    Google Scholar 

  9. Yuan, Y., et al., Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci. Transl. Med. 4(157):157ra143, 2012.

    Article  PubMed  Google Scholar 

  10. 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.

    Article  Google Scholar 

  11. 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.

    Google Scholar 

  12. 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.

    Article  CAS  PubMed  Google Scholar 

  13. 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.

    Google Scholar 

  14. 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.

    Article  PubMed Central  Google Scholar 

  15. 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.

    Article  CAS  PubMed  Google Scholar 

  16. Madabhushi, A., and Lee, G., Image analysis and machine learning in digital pathology: Challenges and opportunities. Med. Image Anal. 33:170–175, 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 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.

  18. 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.

    Article  PubMed  Google Scholar 

  19. 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.

    Google Scholar 

  20. 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.

    Article  PubMed  Google Scholar 

  21. 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.

    Article  PubMed  Google Scholar 

  22. 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.

    Article  PubMed  Google Scholar 

  23. 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.

    Article  PubMed  Google Scholar 

  24. Huang, P.-W., and Lai, Y.-H., Effective segmentation and classification for HCC biopsy images. Pattern Recognit. 43(4):1550–1563, Apr. 2010.

    Article  Google Scholar 

  25. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mouelhi, A., Sayadi, M., and Fnaiech, F., c. In: Communications, Computing and Control Applications (CCCA), 2011 International Conference on, pp. 1–6, 2011.

    Google Scholar 

  27. Wienert, S., et al., Detection and segmentation of cell nuclei in virtual microscopy images: A minimum-model approach. Sci. Rep. 2:503, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  28. 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.

    Chapter  Google Scholar 

  29. 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.

    Chapter  Google Scholar 

  30. 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.

    Article  Google Scholar 

  31. Boddeti, V. N., Kanade, T., and Kumar, B. V. K. V., Correlation Filters for Object Alignment. 2013, pp. 2291–2298.

  32. 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.

    Google Scholar 

  33. Savvides, M., Kumar, B. V., and Khosla, P., Face verification using correlation filters. 3rd IEEE Autom. Identif. Adv. Technol., 56–61, 2002.

  34. 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.

  35. Proakis, J.G., and Manolakis, D.K., Digital Signal Processing, 4th edn. N.J: Pearson, Upper Saddle River, 2006.

    Google Scholar 

  36. 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.

    Chapter  Google Scholar 

  37. Schölkopf, B. and Smola, A. J., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.

  38. Hofmann, M., Support vector machines—Kernels and the kernel trick. Notes. 26, 2006.

  39. Jäkel, F., Schölkopf, B., and Wichmann, F.A., A tutorial on kernel methods for categorization. J. Math. Psychol. 51(6):343–358, 2007.

    Article  Google Scholar 

  40. Shawe-Taylor, J., and Cristianini, N., Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.

  41. Schölkopf, B., and Burges, C.J.C., Advances in Kernel Methods: Support Vector Learning. MIT Press, 1999.

  42. Rao, K.R., Kim, D.N., and Hwang, J.-J., Fast Fourier Transform - Algorithms and Applications, 1st edn. Springer Publishing Company, Incorporated, 2010.

  43. Bankman, I.N., Handbook of Medical Imaging: Processing and Analysis Management. Academic Press, 2000.

  44. Chitode, J.S., Digital Signal Processing. Technical Publications, 2009.

  45. 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.

    CAS  Google Scholar 

  46. Harris, and Fredric, J., On the use of windows for harmonic analysis with the discrete Fourier transform. Proc. IEEE:51–83, 1978.

  47. Alpaydin, E., Introduction to Machine Learning. MIT Press, 2014.

  48. 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.

    Article  Google Scholar 

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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.

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Authors and Affiliations

  1. 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

  2. Department of Computer Science, University of Warwick, Coventry, UK

    Nasir Rajpoot

  3. Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, PO Nilore, Islamabad, Pakistan

    Muhammad Arif

Authors
  1. Asif Ahmad
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  2. Amina Asif
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  3. Nasir Rajpoot
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Correspondence to Asif Ahmad.

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This article is part of the Topical Collection on Image & Signal Processing

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Ahmad, A., Asif, A., Rajpoot, N. et al. Correlation Filters for Detection of Cellular Nuclei in Histopathology Images. J Med Syst 42, 7 (2018). https://doi.org/10.1007/s10916-017-0863-8

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  • DOI: https://doi.org/10.1007/s10916-017-0863-8

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