Fuzzy local information C-means algorithm for histopathological image segmentation

Mustafa Çetin; Zümray Dokur; Tamer Ölmez

doi:10.1109/EBBT.2019.8742034

Fuzzy local information C-means algorithm for histopathological image segmentation

Mustafa Çetin
, Zümray Dokur
, Tamer Ölmez

Elektronik ve Hab.Müh.Bölümü

Istanbul Technical University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

7 Atıf (Scopus)

Özet

Accurate analysis of cellular structures has great importance for cancer diagnosis in histopathological images. Manual analysis of sections carried out by pathologists is time-consuming and costly. Analysis of cell structures with computer aid supports pathologists to diagnose cancer easily. In this paper, automated cell nuclei segmentation from histopathological images is investigated by using Fuzzy Local Information C-means Clustering (FLICM) Method. The Cancer Genome Atlas data set annotated by expert pathologists is used to evaluate the method. Compared with the other related studies, the highest f-measure and overlap values are obtained with this method.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781728110134
DOI'lar	https://doi.org/10.1109/EBBT.2019.8742034
Yayın durumu	Yayınlandı - Nis 2019
Etkinlik	2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 - Istanbul, Türkiye Süre: 24 Nis 2019 → 26 Nis 2019

Yayın serisi

Adı	2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019
Ülke/Bölge	Türkiye
Şehir	Istanbul
Periyot	24/04/19 → 26/04/19

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

SKH 3 Sağlık ve Kaliteli Yaşam

Belgeye Erişim

10.1109/EBBT.2019.8742034

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Çetin, M., Dokur, Z., & Ölmez, T. (2019). Fuzzy local information C-means algorithm for histopathological image segmentation. In 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 Makale 8742034 (2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EBBT.2019.8742034

Çetin, Mustafa ; Dokur, Zümray ; Ölmez, Tamer. / Fuzzy local information C-means algorithm for histopathological image segmentation. 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019).

@inproceedings{93850116a3744b92b5806b469027e53d,

title = "Fuzzy local information C-means algorithm for histopathological image segmentation",

abstract = "Accurate analysis of cellular structures has great importance for cancer diagnosis in histopathological images. Manual analysis of sections carried out by pathologists is time-consuming and costly. Analysis of cell structures with computer aid supports pathologists to diagnose cancer easily. In this paper, automated cell nuclei segmentation from histopathological images is investigated by using Fuzzy Local Information C-means Clustering (FLICM) Method. The Cancer Genome Atlas data set annotated by expert pathologists is used to evaluate the method. Compared with the other related studies, the highest f-measure and overlap values are obtained with this method.",

keywords = "Clustering, Computer aided diagnosis, Fuzzy c-means, Fuzzy local information c-means, Histopathological image segmentation, Nuclei segmentation",

author = "Mustafa {\c C}etin and Z{\"u}mray Dokur and Tamer {\"O}lmez",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 ; Conference date: 24-04-2019 Through 26-04-2019",

year = "2019",

month = apr,

doi = "10.1109/EBBT.2019.8742034",

language = "English",

series = "2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019",

address = "United States",

}

Çetin, M, Dokur, Z & Ölmez, T 2019, Fuzzy local information C-means algorithm for histopathological image segmentation. in 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019., 8742034, 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019, Institute of Electrical and Electronics Engineers Inc., 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019, Istanbul, Türkiye, 24/04/19. https://doi.org/10.1109/EBBT.2019.8742034

Fuzzy local information C-means algorithm for histopathological image segmentation. / Çetin, Mustafa; Dokur, Zümray ; Ölmez, Tamer.
2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8742034 (2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - Fuzzy local information C-means algorithm for histopathological image segmentation

AU - Çetin, Mustafa

AU - Dokur, Zümray

AU - Ölmez, Tamer

PY - 2019/4

Y1 - 2019/4

N2 - Accurate analysis of cellular structures has great importance for cancer diagnosis in histopathological images. Manual analysis of sections carried out by pathologists is time-consuming and costly. Analysis of cell structures with computer aid supports pathologists to diagnose cancer easily. In this paper, automated cell nuclei segmentation from histopathological images is investigated by using Fuzzy Local Information C-means Clustering (FLICM) Method. The Cancer Genome Atlas data set annotated by expert pathologists is used to evaluate the method. Compared with the other related studies, the highest f-measure and overlap values are obtained with this method.

AB - Accurate analysis of cellular structures has great importance for cancer diagnosis in histopathological images. Manual analysis of sections carried out by pathologists is time-consuming and costly. Analysis of cell structures with computer aid supports pathologists to diagnose cancer easily. In this paper, automated cell nuclei segmentation from histopathological images is investigated by using Fuzzy Local Information C-means Clustering (FLICM) Method. The Cancer Genome Atlas data set annotated by expert pathologists is used to evaluate the method. Compared with the other related studies, the highest f-measure and overlap values are obtained with this method.

KW - Clustering

KW - Computer aided diagnosis

KW - Fuzzy c-means

KW - Fuzzy local information c-means

KW - Histopathological image segmentation

KW - Nuclei segmentation

UR - https://www.scopus.com/pages/publications/85068546293

U2 - 10.1109/EBBT.2019.8742034

DO - 10.1109/EBBT.2019.8742034

M3 - Conference contribution

AN - SCOPUS:85068546293

T3 - 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019

BT - 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019

Y2 - 24 April 2019 through 26 April 2019

ER -

Çetin M, Dokur Z , Ölmez T. Fuzzy local information C-means algorithm for histopathological image segmentation. In 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8742034. (2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019). doi: 10.1109/EBBT.2019.8742034

Fuzzy local information C-means algorithm for histopathological image segmentation

Özet

Yayın serisi

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Bibliyografik not

BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

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