Material
Abstract
A vast amount of toxicological data can be obtained from feature analysis of cells treated in vitro. However, this requires microscopic image segmentation of cells. To this end, we propose a new strategy, namely Supervised Normalized Cut Segmentation (SNCS), to segment cells that partially overlap and have a large amount of curved edges. SNCS approach is a machine learning based method, where loosely annotated images are used first to train and optimise parameters, and then the optimal parameters are inserted into a Normalized Cut segmentation process. Furthermore, we compare our segmentation results using SNCS to another four classical and two state-of-the-art methods. The overall experimental result shows the usefulness and effectiveness of our method over the six comparison methods.
BibTeX
@inproceedings{Huang2016Stemcellmicroscopic,
address = {Piscataway, NJ},
author = {X. Huang and C. Li and M. Shen and K. Shirahama and J. Nyffeler and M. Leist and M. Grzegorzek and O. Deussen},
booktitle = {IEEE International Conference on Image Processing},
doi = {10.1109/ICIP.2016.7533139},
editor = {Karam, Lina},
isbn = {978-1-4673-9961-6},
pages = {4140--4144},
publisher = {IEEE},
title = {Stem cell microscopic image segmentation using supervised normalized cuts},
year = {2016},
url = {http://graphics.uni-konstanz.de/publikationen/Huang2016Stemcellmicroscopic},
}