Research Article
Cell Detection and Counting Method Based on Connected Domain of Binary Image
@INPROCEEDINGS{10.1007/978-3-030-69066-3_1, author={Junwen Si and Chuanchuan Zhu and Xufen Xie}, title={Cell Detection and Counting Method Based on Connected Domain of Binary Image}, proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings}, proceedings_a={AICON}, year={2021}, month={7}, keywords={Binarization Morphology Connected domain Cell counting}, doi={10.1007/978-3-030-69066-3_1} }- Junwen Si
Chuanchuan Zhu
Xufen Xie
Year: 2021
Cell Detection and Counting Method Based on Connected Domain of Binary Image
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_1
Abstract
Cell counting plays an important role in biomedical research. There are always some phenomena such as indistinct intervals and target adhesion in cell images, which leads to poor segmentation effect and therefore inaccurate counting. In view of this situation, based on image binarization technology, this paper proposed a rapid cell count method combining mathematical morphology and connected domain labeling in which the cell images can be grayed, USM sharpened, binarized, morphologically processed, and connected domain labeled, and ultimately the number of cells could be calculated. The experimental results show that this method can effectively complete the segmentation of sparse cell images and intensive cell images, and the counting error is less than 5%.
