A Computer-Aided System for Automatic Mitosis Detection from Breast Cancer Histological Slide Images based on Stiffness Matrix and Feature Fusion

ISSN: 2212-392X (Online)
ISSN: 1574-8936 (Print)

Volume 12, 6 Issues, 2017

Download PDF Flyer

Current Bioinformatics

This journal supports open access

Aims & ScopeAbstracted/Indexed in

Submit Abstracts Online Submit Manuscripts Online

Yi-Ping Phoebe Chen
Department of Computer Science and Information Technology
La Trobe University

View Full Editorial Board

Subscribe Purchase Articles Order Reprints

Current: 0.77
5 - Year: 0.93

A Computer-Aided System for Automatic Mitosis Detection from Breast Cancer Histological Slide Images based on Stiffness Matrix and Feature Fusion

Current Bioinformatics, 10(4): 476-493.

Author(s): Ashkan Tashk, Mohammad Sadegh Helfroush, Habibollah Danyali and Mojgan Akbarzadeh.

Affiliation: Department of Electrical and Electronics Engineering, Shiraz University of Technology (SUTECH), Shiraz, Iran.


Background: Nowadays, pathologists grade breast cancer histopathology slides by microscopes based on Nottingham as an international standard. In this standard, three factors are scored. One of these factors is mitotic counting. This counting is a rigid and time consuming activity which suffers from conflicts in inter- and intra- observations.

Objective: To prevent the drawbacks occurred during mitotic counting, the experts fetch up to this fact that it is essential to employ some well-designed and organized computer-aided diagnosis (CAD) systems. For achieving this purpose, an innovative and operational automatic mitosis detection system is proposed in this paper.

Methods: The proposed system constitutes of several image processing stages such as preprocessing, segmentation, feature extraction and supervised classification. The main contribution of the proposed system is in features extraction stage. It efficiently discriminates mitosis objects from non-mitosis ones based on significant features of stiffness matrix (SM) fused by other effective statistical texture features. SM includes various types of features useful and conventional in the study of histopathology slide images.

Results: The proposed CAD system is fully software implemented. The datasets are given to the support vector machine classifier with non-linear kernels for different ratio of training and testing data. A maximum F-measure of 78.42% for scanner A dataset and 83.33% for scanner H are achieved.

Conclusions: The experimental results, demonstrate that the proposed CAD system out performs the other automatic mitosis detection methods proposed in the current literature. It can also be applied to both scanner A and H with no performance and throughput reduction.


completed local binary pattern (CLBP); F-measure criterion; Histology slide images; Mitosis Counting; statisticalmoment entropy (SME); stiffness matrix (SM).

Purchase Online Order Reprints Order Eprints Rights and Permissions

Article Details

Volume: 10
Issue Number: 4
First Page: 476
Last Page: 493
Page Count: 18
DOI: 10.2174/1574893609666140529233721
Price: $58

Related Journals

Webmaster Contact: urooj@benthamscience.org Copyright © 2016 Bentham Science