Mitosis Detection in Breast Cancer Histological Images with Mathematical Morphology

被引：0

作者：

Aptoula, Erchan ^{[1
]}

Courty, Nicolas ^{[2
]}

Lefevre, Sebastien ^{[2
]}

机构：

[1] Okan Univ, Bilgisayar Muhendisligi Bolumu, Istanbul, Turkey

[2] South Britanny Univ, Bilgisayar Muhendisligi Bolumu, Vannes, France

来源：

2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2013年

关键词：

Mathematical morphology; watershed transform; texture description; circular covariance histogram;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

One of the most important outcome predictors of malignant tumors is the mitotic count, i.e. the division speed of cells. This value is computed from the patient's tissue samples by medical experts, that count each mitosis case one by one under a microscope, and as such it is a time consuming process. In order to accelerate it, we present in this paper a system capable of mitosis detection from histological breast cancer images. To this end we have developed a fully automatic solution based on mathematical morphology. The proposed approach has achieved the 10th best performance among 14 teams at the international mitosis detection contest organized by ICPR' 12.

引用

页数：4

共 50 条

[21] A CAD Mitosis Detection System From Breast Cancer Histology Images based on Fused Features
Tashk, Ashkan
Helfroush, Mohammad Sadegh
Danyali, Habibollah
Akbarzadeh-Jahromi, Mojgan
2014 22ND IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE), 2014, : 1924 - 1927
[22] PartMitosis: A Partially Supervised Deep Learning Framework for Mitosis Detection in Breast Cancer Histopathology Images
Sebai, Meriem
Wang, Tianjiang
Al-Fadhli, Saad Ali
IEEE ACCESS, 2020, 8 : 45133 - 45147
[23] Medical images edge detection based on mathematical morphology
Zhao Yu-qian
Gui Wei-hua
Chen Zhen-cheng
Tang Jing-tian
Li Ling-yun
2005 27TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7, 2005, : 6492 - 6495
[24] Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images
Ortega, Samuel
Halicek, Martin
Fabelo, Himar
Guerra, Raul
Lopez, Carlos
Lejaune, Marylene
Godtliebsen, Fred
Callico, Gustavo M.
Fei, Baowei
MEDICAL IMAGING 2020: DIGITAL PATHOLOGY, 2021, 11320
[25] Mitosis Detection in Breast Cancer Histopathology Images Using Statistical, Color and Shape-Based Features
Mahmood, Tahir
Ziauddin, Sheikh
Shahid, Ahmad R.
Safi, Asad
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2018, 8 (05) : 932 - 938
[26] A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images
Sohail, Anabia
Khan, Asifullah
Wahab, Noorul
Zameer, Aneela
Khan, Saranjam
SCIENTIFIC REPORTS, 2021, 11 (01)
[27] Mitosis detection techniques in H&E stained breast cancer pathological images: A comprehensive review
Pan, Xipeng
Lu, Yinghua
Lan, Rushi
Liu, Zhenbing
Qin, Zujun
Wang, Huadeng
Liu, Zaiyi
COMPUTERS & ELECTRICAL ENGINEERING, 2021, 91
[28] A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images
Anabia Sohail
Asifullah Khan
Noorul Wahab
Aneela Zameer
Saranjam Khan
Scientific Reports, 11
[29] Efficient Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using YOLOv5 Model
Lijo, Jithy
Saleema, J. S.
JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY, 2024, 15 (10) : 1184 - 1192
[30] Assessing Domain Adaptation Techniques for Mitosis Detection in Multi-scanner Breast Cancer Histopathology Images
Breen, Jack
Zucker, Kieran
Orsi, Nicolas M.
Ravikumar, Nishant
BIOMEDICAL IMAGE REGISTRATION, DOMAIN GENERALISATION AND OUT-OF-DISTRIBUTION ANALYSIS, 2022, 13166 : 14 - 22

← 1 2 3 4 5 →