NDLI: An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification

Content Provider	IEEE Xplore Digital Library
Author	Tashk, A. Helfroush, M.S. Danyali, H. Akbarzadeh, M.
Copyright Year	2013
Description	Author affiliation: Dept. of Pathology, Shiraz Univ. of Med. Sci., Shiraz, Iran (Akbarzadeh, M.) \|\| Electr. & Electron. Eng. Deartment, Shiraz Univ. of Technol. (SUTECH), Shiraz, Iran (Tashk, A.; Helfroush, M.S.; Danyali, H.)
Abstract	Study of histopathological cancerous tissue is one of the most reliable ways to grade various types of cancers. The result of grading helps the physicians to diagnose and prescribe suitable prognosis. The focus of this paper is on a CAD for automatic analysis of breast cancer histopathological Images to count mitosis as an important criteria for the breast cancer grading. To achieve this aim, sets of specific digital histopathological data are used which are captured by particular microscopic scanners named as Aperio XT and Hamamatsu NanoZoomer scanners. In the proposed method, these acquired images are employed and processed based on digital image processing approaches like 2-D anisotropic diffusion as a pre-process and morphological process. For extraction of pixel-wise features from predetermined mitotic regions, an statistical approach based on color information such as maximum likelihood estimation is employed. To prevent misclassification of mitosis and non-mitosis objects, an object-wise completed local binary pattern (CLBP) is proposed to extract texture features robust against rotation and color-level changes, and finally support vector machine (SVM) is used to classify the extracted feature vectors. Having computed the evaluation criteria, our proposed method performs better f-measure (70.94% for Aperio XT scanner images and 70.11% for Hamamatsu images) among the methods proposed by other participants at ICPR2012 Mitosis detection in breast cancer histopathological images.
Starting Page	406
Ending Page	410
File Size	953663
Page Count	5
File Format	PDF
e-ISBN	9781467364904
DOI	10.1109/IKT.2013.6620101
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-28
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Maximum likelihood estimation Maximum Likelihood Estimation (MLE) Completed Local Binary Pattern (CLBP) Histopathology Microscopy Support Vector Machine (SVM) Feature extraction Pixel-and object-wise classification Breast cancer Biomedical imaging
Content Type	Text
Resource Type	Article

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