An Automated Approach towards Detection of Mitosis in Histopathological Images

Print
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Anand Raj Ulle, T. N. Nagabhushan, Nandini Manoli
10.5120/ijca2018916886

Anand Raj Ulle, T N Nagabhushan and Nandini Manoli. An Automated Approach towards Detection of Mitosis in Histopathological Images. International Journal of Computer Applications 180(35):1-7, April 2018. BibTeX

@article{10.5120/ijca2018916886,
	author = {Anand Raj Ulle and T. N. Nagabhushan and Nandini Manoli},
	title = {An Automated Approach towards Detection of Mitosis in Histopathological Images},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2018},
	volume = {180},
	number = {35},
	month = {Apr},
	year = {2018},
	issn = {0975-8887},
	pages = {1-7},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume180/number35/29287-2018916886},
	doi = {10.5120/ijca2018916886},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Generally, the grade of a breast cancer is considered as an ”aggressive potential” in the growth of a tumor. Breast cancer grading is characterized by three important factors, gland formation, nuclear pleomorphism, and mitosis count. In this research, an automated detection of mitosis from histopathological images is presented. From initial experiments, it has been observed that detection of mitosis becomes challenging, due to the similarity in size and shape compared to nonmitosis nuclei. Towards this end, several contributions have been made to automatically detect mitosis nuclei. From an Exhaustive experimentation, it is clear that mitotic texture shows discriminative features when compared to nonmitotic nuclei. To validate the performance of mitosis detection, two datasets from the MITOSIS-ATYPIA-14 challenge is considered. The proposed method is able to achieve 97% overall accuracy after feature reduction

References

  1. Breast cancer pathology. http://pathology.jhu.edu/ breast/grade.php.
  2. Mitosis-challenge-14 database. https:// mitos-atypia-14.grand-challenge.org/.
  3. Abd¨ulkadir Albayrak and G¨okhan Bilgin. Breast cancer mitosis detection in histopathological images with spatial feature extraction. In Sixth International Conference on Machine Vision (ICMV 2013), volume 9067, page 90670L. International Society for Optics and Photonics, 2013.
  4. Vahid Anari, Parvin Mahzouni, and Rasoul Amirfattahi. Computer-aided detection of proliferative cells and mitosis index in immunohistichemically images of meningioma. In Machine Vision and Image Processing (MVIP), 2010 6th Iranian, pages 1–5. IEEE, 2010.
  5. K Sabeena Beevi, Madhu S Nair, and GR Bindu. A multiclassifier system for automatic mitosis detection in breast histopathology images using deep belief networks. IEEE journal of translational engineering in health and medicine, 5:1–11, 2017.
  6. Ewert Bengtsson, C Wahlby, and Joakim Lindblad. Robust cell image segmentation methods. Pattern Recognition and Image Analysis C/c of Raspoznavaniye Obrazov i Analiz Izobrazhenii., 14(2):157–167, 2004.
  7. Fei Dong, Humayun Irshad, Eun-Yeong Oh, Melinda F Lerwill, Elena F Brachtel, Nicholas C Jones, Nicholas W Knoblauch, Laleh Montaser-Kouhsari, Nicole B Johnson, Luigi KF Rao, et al. Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. PloS one, 9(12):e114885, 2014.
  8. H. Fatakdawala, J. Xu, A. Basavanhally, G. Bhanot, S. Ganesan, M. Feldman, J.E. Tomaszewski, and A Madabhushi. Expectationmaximization-driven geodesic active contour with overlap resolution (emagacor): Application to lymphocyte segmentation on breast cancer histopathology. Transactions on Biomedical Engineering, 2010.
  9. Mary M Galloway. Texture analysis using grey level run lengths. NASA STI/Recon Technical Report N, 75, 1974.
  10. Robert M Haralick, Karthikeyan Shanmugam, et al. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6):610–621, 1973.
  11. Peter Haub and Tobias Meckel. A model based survey of colour deconvolution in diagnostic brightfield microscopy: Error estimation and spectral consideration. Scientific reports, 5:12096, 2015.
  12. Lei He, L Rodney Long, Sameer Antani, and George R Thoma. Histology image analysis for carcinoma detection and grading. Computer methods and programs in biomedicine, 107(3):538–556, 2012.
  13. Michael Held, Michael HA Schmitz, Bernd Fischer, Thomas Walter, Beate Neumann, Michael H Olma, Matthias Peter, Jan Ellenberg, and Daniel W Gerlich. Cellcognition: timeresolved phenotype annotation in high-throughput live cell imaging. Nature methods, 7(9):747, 2010.
  14. Humayun Irshad, Sepehr Jalali, Ludovic Roux, Daniel Racoceanu, Lim Joo Hwee, Gilles Le Naour, and Fr´ed´erique Capron. Automated mitosis detection using texture, sift features and hmax biologically inspired approach. Journal of pathology informatics, 4(Suppl), 2013.
  15. Adnan Mujahid Khan, Nasir Rajpoot, Darren Treanor, and Derek Magee. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Transactions on Biomedical Engineering, 61(6):1729–1738, 2014.
  16. Saravana Kumar, Sim Heng Ong, Surendra Ranganath, Fook Tim Chew, and Tan Ching Ong. Segmentation of microscope cell images via adaptive eigenfilters. In Image Processing, 2004. ICIP’04. 2004 International Conference on, volume 1, pages 135–138. IEEE, 2004.
  17. Christopher D Malon and Eric Cosatto. Classification of mitotic figures with convolutional neural networks and seeded blob features. Journal of pathology informatics, 4, 2013.
  18. Shreshtha Malvia, Sarangadhara Appalaraju Bagadi, Uma S Dubey, and Sunita Saxena. Epidemiology of breast cancer in indian women. Asia-Pacific Journal of Clinical Oncology, 2017.
  19. Boudjelal Meftah, Olivier Lezoray, Michel Lecluse, and Abdelkader Benyettou. Cell microscopic segmentation with spiking neuron networks. In International Conference on Artificial Neural Networks, pages 117–126. Springer, 2010.
  20. Ville Ojansivu and Janne Heikkil¨a. Blur insensitive texture classification using local phase quantization. In International conference on image and signal processing, pages 236–243. Springer, 2008.
  21. Angshuman Paul and Dipti Prasad Mukherjee. Mitosis detection for invasive breast cancer grading in histopathological images. IEEE Transactions on Image Processing, 24(11):4041–4054, 2015.
  22. Hady Ahmady Phoulady, Dmitry B Goldgof, Lawrence O Hall, and Peter R Mouton. Nucleus segmentation in histology images with hierarchical multilevel thresholding. In SPIE Medical Imaging, pages 979111–979111. International Society for Optics and Photonics, 2016.
  23. Vincent Roullier, Olivier L´ezoray, Vinh-Thong Ta, and Abderrahim Elmoataz. Multi-resolution graph-based analysis of histopathological whole slide images: Application to mitotic cell extraction and visualization. Computerized Medical Imaging and Graphics, 35(7-8):603–615, 2011.
  24. Arnout C Ruifrok, Dennis A Johnston, et al. Quantification of histochemical staining by color deconvolution. Analytical and quantitative cytology and histology, 23(4):291–299, 2001.
  25. Johannes Schindelin, Ignacio Arganda-Carreras, Erwin Frise, Verena Kaynig, Mark Longair, Tobias Pietzsch, Stephan Preibisch, Curtis Rueden, Stephan Saalfeld, Benjamin Schmid, et al. Fiji: an open-source platform for biologicalimage analysis. Nature methods, 9(7):676, 2012.
  26. Olcay Sertel, Umit V Catalyurek, Hiroyuki Shimada, and Metin N Gurcan. Computer-aided prognosis of neuroblastoma: Detection of mitosis and karyorrhexis cells in digitized histological images. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pages 1433–1436. IEEE, 2009.
  27. Christoph Sommer, Luca Fiaschi, Fred A Hamprecht, and Daniel W Gerlich. Learning-based mitotic cell detection in histopathological images. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 2306–2309. IEEE, 2012.
  28. Christoph Sommer, Christoph Straehle, Ullrich Koethe, and Fred A Hamprecht. Ilastik: Interactive learning and segmentation toolkit. In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, pages 230–233. IEEE, 2011.
  29. Lisa A Teot, Richard Sposto, Anita Khayat, Stephen Qualman, Gregory Reaman, and David Parham. The problems and promise of central pathology review: development of a standardized procedure for the children’s oncology group. Pediatric and Developmental Pathology, 10(3):199–207, 2007.
  30. M Veta, A Huisman, M.A. Viergever, van Diest, P.J., and J.P Pluim. Marker-controlled watershed segmentation of nuclei in h&e stained breast cancer biopsy images. Biomedical Imaging, 2011.
  31. P. Viswanathan. Detection of leukemia based on morphological contour segmentation. Procedia Computer Science, 2015.
  32. Carolina W¨ahlby, I-M SINTORN, Fredrik Erlandsson, Gunilla Borgefors, and Ewert Bengtsson. Combining intensity, edge and shape information for 2d and 3d segmentation of cell nuclei in tissue sections. Journal of microscopy, 215(1):67– 76, 2004.
  33. Haibo Wang, Angel Cruz Roa, Ajay N Basavanhally, Hannah L Gilmore, Natalie Shih, Mike Feldman, John Tomaszewski, Fabio Gonzalez, and Anant Madabhushi. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. Journal of Medical Imaging, 1(3):034003, 2014.

Keywords

Histopathological Images, Mitosis, Texture features, Patch extraction, Digital Pathology