Parasite Ovum Automatic Recognition in the High Noise of Microscopic Image

DOI

10.2991/epee-16.2016.54How to use a DOI?

Keywords

parasite ovum; threshold segmentation; mathematical morphology; feature extraction; BP neural network; automatic identification

Abstract

An automatic recognition method of parasitic ovum under the high noise is studied in this thesis. Considering the status of the complex background in the parasitic ovum images, firstly, the corresponding method is applied to image preprocessing, then, the noise removing and edge segmentation combined with threshold segmentation and mathematical morphology operation are emphatically studied, then feature extraction and parasitic ovum classification are processed, gaining a set of algorithm process to recognize parasitic ovum in complex background rapidly and accurately. Finally, this method is tested by the recognition experiments of three kinds of parasitic ovum, the results verify the effectiveness of the method.

Copyright

© 2016, 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  - Fengshou Zhang
AU  - Xin Meng
AU  - Zhigang Hu
AU  - Siwen Li
PY  - 2016/10
DA  - 2016/10
TI  - Parasite Ovum Automatic Recognition in the High Noise of Microscopic Image
BT  - Proceedings of the 2016 International Conference on Energy, Power and Electrical Engineering
PB  - Atlantis Press
SP  - 241
EP  - 243
SN  - 2352-5401
UR  - https://doi.org/10.2991/epee-16.2016.54
DO  - 10.2991/epee-16.2016.54
ID  - Zhang2016/10
ER  -