Automatic segmentation of clustered breast cancer cells based on modified watershed algorithm and concavity points searching.
- Author:
Zhen TONG
1
;
Lixin PU
;
Fangjie DONG
Author Information
1. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China. elitetongzhen@126.com
- Publication Type:Journal Article
- MeSH:
Algorithms;
Breast Neoplasms;
pathology;
Cell Separation;
Epithelial Cells;
chemistry;
pathology;
Female;
Humans;
Image Processing, Computer-Assisted;
Immunohistochemistry;
methods
- From:
Journal of Biomedical Engineering
2013;30(4):692-696
- CountryChina
- Language:Chinese
-
Abstract:
As a common malignant tumor, breast cancer has seriously affected women's physical and psychological health even threatened their lives. Breast cancer has even begun to show a gradual trend of high incidence in some places in the world. As a kind of common pathological assist diagnosis technique, immunohistochemical technique plays an important role in the diagnosis of breast cancer. Usually, Pathologists isolate positive cells from the stained specimen which were processed by immunohistochemical technique and calculate the ratio of positive cells which is a core indicator of breast cancer in diagnosis. In this paper, we present a new algorithm which was based on modified watershed algorithm and concavity points searching to identify the positive cells and segment the clustered cells automatically, and then realize automatic counting. By comparison of the results of our experiments with those of other methods, our method can exactly segment the clustered cells without losing any geometrical cell features and give the exact number of separating cells.
