Segmentation of Overlapping Cervical Cells in Microscopic Images with Superpixel Partitioning and Cell-Wise Contour Refinement
Abstract
Segmentation of cervical cells in microscopic images is an important task for computer-aided diagnosis of cervical cancer. However, their segmentation is challenging due to inhomogeneous cell cytoplasm and the overlap between the cells. In this paper, we propose an automatic segmentation method for multiple overlapping cervical cells in microscopic images using superpixel partitioning and cell-wise contour refinement. First, the cell masses are detected by superpixel generation and triangle thresholding. Then, nuclei of cells are extracted by local thresholding and outlier removal. Finally, cell cytoplasm is initially segmented by superpixel partitioning and refined by cell-wise contour refinement with graph cuts. In experiments, our method showed competitive performances in two public challenge data sets compared to the state-of-the-art methods.
Cite
Text
Lee and Kim. "Segmentation of Overlapping Cervical Cells in Microscopic Images with Superpixel Partitioning and Cell-Wise Contour Refinement." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016. doi:10.1109/CVPRW.2016.172Markdown
[Lee and Kim. "Segmentation of Overlapping Cervical Cells in Microscopic Images with Superpixel Partitioning and Cell-Wise Contour Refinement." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016.](https://mlanthology.org/cvprw/2016/lee2016cvprw-segmentation/) doi:10.1109/CVPRW.2016.172BibTeX
@inproceedings{lee2016cvprw-segmentation,
title = {{Segmentation of Overlapping Cervical Cells in Microscopic Images with Superpixel Partitioning and Cell-Wise Contour Refinement}},
author = {Lee, Hansang and Kim, Junmo},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2016},
pages = {1367-1373},
doi = {10.1109/CVPRW.2016.172},
url = {https://mlanthology.org/cvprw/2016/lee2016cvprw-segmentation/}
}