%0 Journal Article
%T Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization
%A Ramin Nateghi
%A Habibollah Danyali
%A Mohammad Sadegh Helfroush
%A Ashkan Tashk
%J Computational Biology Journal
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/970898
%X This paper introduces a computer-assisted diagnosis (CAD) system for automatic mitosis detection from breast cancer histopathology slide images. In this system, a new approach for reducing the number of false positives is proposed based on Teaching-Learning-Based optimization (TLBO). The proposed CAD system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In TLBO algorithm, the number of false positives (falsely detected nonmitosis candidates as mitosis ones) is defined as a cost function and, by minimizing it, many of nonmitosis candidates will be removed. Then some color and texture (textural) features such as those derived from cooccurrence and run-length matrices are extracted from the remaining candidates and finally mitotic cells are classified using a specific support vector machine (SVM) classifier. The simulation results have proven the claims about the high performance and efficiency of the proposed CAD system. 1. Introduction Nowadays, one of the most prevalent types of cancers which mostly lead to death is breast cancer [1]. Due to the World Health Organization (WHO) standardizations, there is a system known as Nottingham which is used for breast cancer grading. According to this system, three morphological features known as nuclear polymorphism, tubular formations, and number of mitosis cells are used for grading breast cancer [2]. The diagnosis of breast cancer grade is done by pathologists using histopathology slides. In recent years various computer-assisted diagnosis (CAD) systems on breast cancer diagnosis have been proposed. Such systems provide a great assistance for grading breast cancer samples faster and more accurately. In some of the researches, automatic breast cancer grading systems based on the three breast cancer grading features have been presented [3¨C5]. However, most of the researches on breast cancer histopathology images usually consider only one of the following: nuclear polymorphism [4, 6¨C8], tubule formations [9, 10], or mitosis cells counting [11¨C15]. The amount of dividing cells known as mitosis is essential feature for breast cancer grading. Pathologists count the number of mitoses in 10 distinct microscopic high power fields (HPFs) and, based on the average number of counted mitosis, the related scoring is calculated due to [5]: where is the average number of mitoses in 10£¿HPFs. For counting the number of mitoses in breast cancer histopathology slide images, several automatic methods have been proposed [11¨C15]. Some of these methods have used multispectral
%U http://www.hindawi.com/journals/cbj/2014/970898/