Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images.
Based on the Nottingham criteria, the number of mitosis cells in histopathological slides is an important factor in diagnosis and grading of breast cancer. For manual grading of mitosis cells, histopathology slides of the tissue are examined by pathologists at 40× magnification for each patient. Thi...
| Publicado en: | Journal of Medical Systems Vol. 41; no. 9; pp. 1 - 16 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research Journal Article |
| Publicado: |
Springer Nature
Sep2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=125068547&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125068547 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2017 vid: 41 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 125068547 125068547 125068547 10.1007/s10916-017-0773-9 125068547 ppf: 1 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images. aug: au: Nateghi, Ramin Danyali, Habibollah Helfroush, Mohammad affil: Department of Electrical and Electronics Engineering , Shiraz University of Technology (SUTECH) , Shiraz Iran sug: subj: Breast Neoplasms Physiopathology Cell Cycle Histological Techniques Diagnostic Imaging Cell Physiology Female Breast Neoplasms Diagnosis Neoplasm Grading Technology Automation Data Analysis Female ab: Based on the Nottingham criteria, the number of mitosis cells in histopathological slides is an important factor in diagnosis and grading of breast cancer. For manual grading of mitosis cells, histopathology slides of the tissue are examined by pathologists at 40× magnification for each patient. This task is very difficult and time-consuming even for experts. In this paper, a fully automated method is presented for accurate detection of mitosis cells in histopathology slide images. First a method based on maximum-likelihood is employed for segmentation and extraction of mitosis cell. Then a novel Maximized Inter-class Weighted Mean (MIWM) method is proposed that aims at reducing the number of extracted non-mitosis candidates that results in reducing the false positive mitosis detection rate. Finally, segmented candidates are classified into mitosis and non-mitosis classes by using a support vector machine (SVM) classifier. Experimental results demonstrate a significant improvement in accuracy of mitosis cells detection in different grades of breast cancer histopathological images. pubtype: Academic Journal doctype: equations & formulas pictorial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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