Fully Automated Computer-Assisted Diagnostic Method for Mitosis Detection on Histology Slide Images of Breast Cancer


avatar Fattane Pourakpour 1 , * , avatar Hassan Ghassemian 2 , avatar Ramin Nateghi 3

National Brain Mapping Laboratory, Tehran, Iran
Image Processing and Information Analysis Lab, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

how to cite: Pourakpour F, Ghassemian H, Nateghi R. Fully Automated Computer-Assisted Diagnostic Method for Mitosis Detection on Histology Slide Images of Breast Cancer. Innov J Radiol. 2019;16(Special Issue):e99149. https://doi.org/10.5812/iranjradiol.99149.



Nowadays, advances in the field of medical science, especially the branch of histology, have made it possible to detect cancer, its growth rate, type, and extent of cancer malignancy. According to GLOBOCAN 2012, breast cancer ranks second in terms of prevalence and mortality [1]. The number of mitoses in histology slide images is considered as one of the three significant factors in grading breast cancer. The mitosis count is done manually by pathologists but automating the mitosis count process can decrease its time and costs. Different automatic techniques have been proposed in the literature for breast cancer mitotic counting [2-5].


In this paper, we propose an automated method for accurate mitotic cell detection in breast cancer histology slide images.


To evaluate experiments, we used the Mitos-ICPR 2012 dataset consisting of 50 HPFs with the train-to-test ratio of 70% to 30%, accounting for 35 images for training and 15 images for testing. These 50 HPFs were obtained by analyzing the texture of five different patients. Each slide was stained with standard Hematoxylin and Eosin (H&E) stains and two expert pathologists marked the mitotic cells with labels in 10 selected distinct microscopic HPFs at 40x magnification. The slides were scanned by two slide scanners, including Aperio Scanscope XT and Hamamatsu Nano-zoomer. Totally, there were 326 mitotic cells in this dataset [6]. As shown in Figure 1, the proposed fully automated system consisted of the following stages: segmentation and extraction of mitosis candidates using statistical Gaussian Mixture Model (GMM), feature extraction, and classification using SVM classifier (with different kernels) and decision tree classifier. After candidate extraction using GMM, to identify candidates as mitoses or non-mitoses, it was necessary to extract discriminant and reliable features. In the proposed feature extraction stage, we focused on the extraction of textural features including GLCM, CLBP, and statistical moments of filtered images by the Gabor filter in the RGB color space. Moreover, several shape features were proposed to achieve a better distinction of mitosis from non-mitosis candidates. The proposed shape features were based on calculating the Euclidean distance of boundary pixels from the center of each cell. Two types of textural and shape features were combined with each other to provide the final feature vector with a length of 214.


The visual results of the proposed automated mitosis detection system for a sample histopathology slide image are shown in Figure 2.


The detailed experimental results demonstrated the promising performance of the proposed fully automated method for mitosis detection with F-measure (91.49%).

To see figures and references please refer to the PDF file.

Copyright © 2019, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.