Bacterial image segmentation algorithm based on improved level set

DOI

10.2991/icadme-17.2017.40How to use a DOI?

Keywords

Sewage treatment, Bacteria image segmentation, Level set, CV model, LBF model.

Abstract

The ever - worsening water pollution has prompted the emergence of a large number of sewage treatment plants; meanwhile, the activated sludge process has been developed rapidly. The species, quantity and the stage of growth of the microorganisms in the sewage treatment by activated sludge process is the major determinants of sludge settling performance. So the level set and its improved methods of bacterial image segmentation on CV model and LBF model are studied in the paper, and then the bacterial image is segmented and identified in the sewage treatment process through microscopic examination of activated sludge microorganisms. The results show that LBF variational level set model for bacterial image segmentation is more efficient, stable and robust. Therefore, in sewage treatment, the sludge settling performance can be predicted according to the results of the segmentation, so as to take measures to further improve the process.

Copyright

© 2017, 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/).

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TY  - CONF
AU  - Xianqi Cao
AU  - Jiaqing Miao
PY  - 2017/07
DA  - 2017/07
TI  - Bacterial image segmentation algorithm based on improved level set
BT  - Proceedings of the 2017 7th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2017)
PB  - Atlantis Press
SP  - 204
EP  - 208
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-17.2017.40
DO  - 10.2991/icadme-17.2017.40
ID  - Cao2017/07
ER  -