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International Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2018: Bioinformatics and Biomedical Engineering pp 276–286Cite as

Classification of Breast Cancer Histopathological Images Using KAZE Features

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10814))

Abstract

Breast cancer (BC) is a public health problem of first importance, being the second most common cancer worldwide. BC represents 30.4% of all new cancer cases in the European female population. The diagnosis and differential diagnosis of BC is based on the clinical presentations, physical examinations combined with imaging studies, and confirmed by histopathologic findings. Pathologists’ examination is a time-consuming analysis, susceptible to an interpretation bias mainly caused by the experience of the pathologist and the decrease of attention due to fatigue. Currently, computer-aided detection and diagnosis techniques applied to digital images are assisting the specialists.

In this work, the performance of a pattern recognition system based on KAZE features in combination with Bag-of-Features (BOF) to discriminate between benign and malignant tumours is evaluated on the BreakHis database (7909 images). During the training stage, KAZE keypoints are extracted for every image in the training set. Keypoints are mapped into a histogram vector using K-means clustering. This histogram represents the input to build a binary SVM classifier. In the testing stage, the KAZE keypoints are extracted for every image in the test set, and fed into the cluster model to map them into a histogram vector. This vector is finally fed into the binary SVM training classifier to classify the image.

The experimental evaluation shows the feasibility and effectiveness, in terms of classification accuracy, of the proposed scheme for the binary classification of breast cancer histopathological images with a low magnification factor.

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Notes

  1. 1.

    https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/.

References

  1. World Cancer Research Fund International: Cancer facts and figures - Worldwide data. http://www.wcrf.org/int/cancer-facts-figures/worldwide-data. Accessed 18 Jan 2018

  2. Parks, R.M., Derks, M.G.M., Bastiaannet, E., Cheung, K.L.: Breast cancer epidemiology. In: Wyld, L., Markopoulos, C., Leidenius, M., Senkus-Konefka, E. (eds.) Breast Cancer Management for Surgeons, pp. 19–29. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-56673-3_3

    Chapter  Google Scholar 

  3. Ferlay, J., Steliarova-Foucher, E., Lortet-Tieulent, J., Rosso, S., Coebergh, J.W.W., Comber, H., Forman, D., Bray, F.: Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur. J. Cancer 49, 1374–1403 (2013)

    Article  Google Scholar 

  4. World Health Organization: Breast cancer: prevention and control. http://who.int/cancer/detection/breastcancer/en/index1.html. Accessed 18 Jan 2018

  5. Senkus, E., Kyriakides, S., Ohno, S., Penault-Llorca, F., Poortmans, P., Rutgers, E., Zackrisson, S., Cardoso, F.: Primary breast cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 26, v8–v30 (2015)

    Article  Google Scholar 

  6. Veta, M., Pluim, J.P.W., Van Diest, P.J., Viergever, M.A.: Breast cancer histopathology image analysis: a review. IEEE Trans. Biomed. Eng. 61, 1400–1411 (2014)

    Article  Google Scholar 

  7. Aswathy, M.A., Jagannath, M.: Detection of breast cancer on digital histopathology images: present status and future possibilities. Inform. Med. Unlocked 8, 74–79 (2017)

    Article  Google Scholar 

  8. Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009)

    Article  Google Scholar 

  9. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63, 1455–1462 (2016)

    Article  Google Scholar 

  10. Wang, P., Hu, X., Li, Y., Liu, Q., Zhu, X.: Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Sig. Process. 122, 1–13 (2016)

    Article  Google Scholar 

  11. Kowal, M., Filipczuk, P., Obuchowicz, A., Korbicz, J., Monczak, R.: Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput. Biol. Med. 43, 1563–1572 (2013)

    Article  Google Scholar 

  12. Zhang, Y., Zhang, B., Coenen, F., Lu, W.: Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles. Mach. Vis. Appl. 24, 1405–1420 (2013)

    Article  Google Scholar 

  13. Zheng, Y., Jiang, Z., Shi, J., Ma, Y.: Retrieval of pathology image for breast cancer using PLSA model based on texture and pathological features. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 2304–2308. IEEE (2014)

    Google Scholar 

  14. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: International Joint Conference on Neural Networks (IJCNN 2016), pp. 2560–2567 (2016)

    Google Scholar 

  15. Cruz-Roa, A., Basavanhally, A., González, F., Gilmore, H., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., Madabhushi, A.: Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. Presented at the 20 March 2014

    Google Scholar 

  16. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

  17. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32

    Chapter  Google Scholar 

  18. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2564–2571 (2011)

    Google Scholar 

  19. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)

    Article  MATH  Google Scholar 

  20. Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2548–2555 (2011)

    Google Scholar 

  21. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 886–893 (2005)

    Google Scholar 

  22. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22, 761–767 (2004)

    Article  Google Scholar 

  23. Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retina keypoint. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 510–517 (2012)

    Google Scholar 

  24. Alcantarilla, P.F., Bartoli, A., Davison, A.J.: KAZE features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 214–227. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33783-3_16

    Chapter  Google Scholar 

  25. Demchev, D., Volkov, V., Kazakov, E., Sandven, S.: Feature tracking for sea ice drift retrieval from SAR images. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 330–333. IEEE (2017)

    Google Scholar 

  26. Inoue, R., Goto, T., Hirano, S.: Authenticity inspection by image recognition using feature point matching. In: 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), pp. 1–2. IEEE (2017)

    Google Scholar 

  27. Utaminingrum, F., Kumiawan, T.A., Fauzi, M.A., Wihandika, R.C., Adikara, P.P.: Adaptive human tracking for smart wheelchair. In: 5th International Symposium on Computational and Business Intelligence, ISCBI 2017, pp. 10–13 (2017)

    Google Scholar 

  28. Chen, Y.S., Chien, J.C., Lee, J.D.: KAZE-BOF-based large vehicles detection at night. In: 2016 International Conference on Communication Problem-Solving, ICCP 2016, pp. 2–3 (2016)

    Google Scholar 

  29. Spanhol, F.A., Cavalin, P.R., Oliveira, L.S., Petitjean, C., Heutte, L.: Deep features for breast cancer histopathological image classification. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 1868–1873 (2017)

    Google Scholar 

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Acknowledgement

This work was supported by grant TIN2015-67020-P (Spanish “Ministerio de Economía y Competitividad and FEDER funds).

Author information

Authors and Affiliations

  1. Biomedical Engineering and Telemedicine Research Group, University of Cadiz, Puerto Real, Cadiz, Spain

    Daniel Sanchez-Morillo & Marcial García-Rojo

  2. Department of Automation, Electronics and Computers and Network Architecture, University of Cadiz, Puerto Real, Cadiz, Spain

    Daniel Sanchez-Morillo

  3. Department of Computer Architecture and Technology, CITIC, University of Granada, Granada, Spain

    Jesús González & Julio Ortega

  4. University Hospital Puerta del Mar of Cadiz Cádiz, Cadiz, Spain

    Marcial García-Rojo

Authors
  1. Daniel Sanchez-Morillo
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  2. Jesús González
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  3. Marcial García-Rojo
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  4. Julio Ortega
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Corresponding author

Correspondence to Daniel Sanchez-Morillo .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Francisco Ortuño

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Sanchez-Morillo, D., González, J., García-Rojo, M., Ortega, J. (2018). Classification of Breast Cancer Histopathological Images Using KAZE Features. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10814. Springer, Cham. https://doi.org/10.1007/978-3-319-78759-6_26

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  • DOI: https://doi.org/10.1007/978-3-319-78759-6_26

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