An supervised learning method for overlapping cells
- https://doi.org/10.2991/icmra-15.2015.207How to use a DOI?
- overlapping; non-rigid registration; over-segmentation; template matching
The clustering phenomenon often appears in histopathology image, some cells overlap or touch together to from a big area. It is necessary to design an effective algorithm to separate the clustering cells into single one. We describe a generic method for segmentation microscopy images based on supervised modeling. The main idea is to use the example input segmentations to learn a statistical model of the shape and texture of the structures to be segmented. The segmentation of the test image can be functioned by maximizing the normalized cross correlation between the model and neighborhoods in the test image, accompanied by a final adjustment that utilizes nonrigid registration. This method can effectively and efficiently solve the overlapping and over-segmentation problem.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Pengfei Shen AU - Jie Yang PY - 2015/04 DA - 2015/04 TI - An supervised learning method for overlapping cells BT - Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation PB - Atlantis Press SP - 1071 EP - 1075 SN - 2352-538X UR - https://doi.org/10.2991/icmra-15.2015.207 DO - https://doi.org/10.2991/icmra-15.2015.207 ID - Shen2015/04 ER -