ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Breast Cancer Grading of H&E Stained Histopathology Images


Affiliations
1 Department of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, India
     

   Subscribe/Renew Journal


Breast cancer is the common existing form of cancers amongst women. The automatic image analysis methods have an enormous potential to decrease the workload in a pathology laboratory. The grading of breast cancer histopathology images is used to find the level of breast cancer. The automatic grading of breast cancer histopathology images is a challenging task. In this paper a system for automatic detection of breast cancer grading of H&E stained histopathological images is presented. An image processing techniques such as preprocessing, segmentation, feature extraction and classification are used in this system. The segmentation of nuclei in H&E stained image is performed using color thresholding and maximum entropy thresholding. The features are computed according to Bloom Richardson grading criteria. The decision tree classifier is used to classify input image into three group i.e., low grade, intermediate grade and high grade.

Keywords

Breast Cancer, Histopathology, H&E (Hematoxylin and Eosin), Grading, Bloom Richardson Criteria, Color Thresholding, Maximum Entropy, Decision Tree.
Subscription Login to verify subscription
User
Notifications
Font Size

  • J.R. Dalle, W.K. Leow, D. Racoceanu, A.E. Tutac and T.C. Putti, “Automatic Breast Cancer Grading of Histopathological Images”, Proceedings of Annual Conference on IEEE Engineering in Medicine and Biology Society, pp. 3052-3055, 2008.

  • S. Naik, S. Doyle, S. Agner, A. Madabhushi, M. Feldman and J. Tomaszewski, “Automated Gland and Nuclei Segmentation for Grading of Prostate and Breast Cancer Histopathology”, Proceedings of 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 284-287, 2008.

  • Scott Doyle et al., “Automated Grading of Breast Cancer Histopathology using Spectral Clustering with Textural and Architectural Image Features”, Proceedings of 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 114-117, 2008.

  • Pegah Faridi et al., “Cancerous Nuclei Detection and Scoring in Breast Cancer Histopathological Images”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1-7, 2016.

  • Mitko Veta et al., “Assessment of Algorithms for Mitosis Detection in Breast Cancer Histopathology Images”, Medical Image Analysis, Vol. 20, No. 1, pp. 237-248, 2015.

  • T.K. Ten Kate et al., “Method for Counting Mitoses by Image Processing in Feulgen Stained Breast Cancer Sections”, Cytometry, Vol. 14, No. 3, pp. 241-250, 1993.

  • Dan C. Ciresan et al., “Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks”, Proceedings of International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 1-8, 2013.

  • K. Nguygen, “Automatic Glandular and Tubule Detection in Histological Grading of Breast Cancer”, Proceedings of the SPIE, Vol. 9420, pp. 1-7, 2015.

  • Jean Romain Dalle et al., “Nuclear Pleomorphism Scoring by Selective Cell Nuclei Detection”, Available at: https://www.comp.nus.edu.sg/~leowwk/papers/wacv2009-npscore.pdf.

  • T. Wan et al., “Automated Grading of Breast Cancer Histopathology using Cascaded Ensemble with Combination of Multi-Level Image Features”, Neurocomputing, Vol. 229, pp. 34-44, 2017.

  • Fabio A. Spanhol et al., “A Dataset for Breast Cancer Histopathological Image Classification”, IEEE Transactions on Biomedical Engineering, Vol. 63, No. 7, pp. 1455-1462, 2016.

  • Rafikha Aliana A. Raof, “Comparison of Colour Thresholding Method using RGB and HSI Information for Ziehl-Neelsen Sputum Slide Images”, Proceedings of 10th International Conference on Information Sciences Signal Processing and their Applications, pp. 1-8, 2010.

  • K. Meethongjan and D. Mohamad, “Maximum Entropy-Based Thresholding Algorithm for Face Image Segmentation”, Available at:https://pdfs.semanticscholar.org/9081/8e0ab85b6a5f03cd28bc5c23c90a0971c36e.pdf.


Abstract Views: 256

PDF Views: 0




  • Breast Cancer Grading of H&E Stained Histopathology Images

Abstract Views: 256  |  PDF Views: 0

Authors

Vijay M. Mane
Department of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, India
Nikhil Tagalpallewar
Department of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, India

Abstract


Breast cancer is the common existing form of cancers amongst women. The automatic image analysis methods have an enormous potential to decrease the workload in a pathology laboratory. The grading of breast cancer histopathology images is used to find the level of breast cancer. The automatic grading of breast cancer histopathology images is a challenging task. In this paper a system for automatic detection of breast cancer grading of H&E stained histopathological images is presented. An image processing techniques such as preprocessing, segmentation, feature extraction and classification are used in this system. The segmentation of nuclei in H&E stained image is performed using color thresholding and maximum entropy thresholding. The features are computed according to Bloom Richardson grading criteria. The decision tree classifier is used to classify input image into three group i.e., low grade, intermediate grade and high grade.

Keywords


Breast Cancer, Histopathology, H&E (Hematoxylin and Eosin), Grading, Bloom Richardson Criteria, Color Thresholding, Maximum Entropy, Decision Tree.

References