Automatic detection method of bacteria

Jun Zhang; Qieshi Zhang

doi:10.1109/ICIA.2007.4295733

Automatic detection method of bacteria

Jun Zhang^*, Qieshi Zhang

^*Corresponding author for this work

Information, Production and Systems Research Center

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In the bacteria images automatic analysis system, bacteria detection is a vital step. According to the characteristics of sample images, this paper proposed a method based on color space conversion, self-organizing feature map (SOFM) neural network and morphological operations. First, the conversion formula is built to convert RGB color space to CMYK color space. Second, according to the information of M channel in the CMYK color space, SOFM is used to reduce gray level and then the binary image can be got. Third, morphological operation is used to distinguish and count single bacterium, mass bacteria, and color drop. With abundant samples, experimental results indicate our method can extract the objects both accurately and availably.

Original language	English
Title of host publication	Proceedings of the 2007 International Conference on Information Acquisition, ICIA
Pages	237-240
Number of pages	4
DOIs	https://doi.org/10.1109/ICIA.2007.4295733
Publication status	Published - 2007
Event	International Conference on Information Acquisition, ICIA 2007 - Jeju City Duration: 2007 Jul 9 → 2007 Jul 11

Other

Other	International Conference on Information Acquisition, ICIA 2007
City	Jeju City
Period	07/7/9 → 07/7/11

Keywords

Color space
Morphological operation
Self-organizing feature map (SOFM)

ASJC Scopus subject areas

Information Systems
Information Systems and Management

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10.1109/ICIA.2007.4295733

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@inproceedings{3a3dc8156dcc437d88957fb76f349a93,

title = "Automatic detection method of bacteria",

abstract = "In the bacteria images automatic analysis system, bacteria detection is a vital step. According to the characteristics of sample images, this paper proposed a method based on color space conversion, self-organizing feature map (SOFM) neural network and morphological operations. First, the conversion formula is built to convert RGB color space to CMYK color space. Second, according to the information of M channel in the CMYK color space, SOFM is used to reduce gray level and then the binary image can be got. Third, morphological operation is used to distinguish and count single bacterium, mass bacteria, and color drop. With abundant samples, experimental results indicate our method can extract the objects both accurately and availably.",

keywords = "Color space, Morphological operation, Self-organizing feature map (SOFM)",

author = "Jun Zhang and Qieshi Zhang",

year = "2007",

doi = "10.1109/ICIA.2007.4295733",

language = "English",

isbn = "1424412196",

pages = "237--240",

booktitle = "Proceedings of the 2007 International Conference on Information Acquisition, ICIA",

note = "International Conference on Information Acquisition, ICIA 2007 ; Conference date: 09-07-2007 Through 11-07-2007",

}

TY - GEN

T1 - Automatic detection method of bacteria

AU - Zhang, Jun

AU - Zhang, Qieshi

PY - 2007

Y1 - 2007

N2 - In the bacteria images automatic analysis system, bacteria detection is a vital step. According to the characteristics of sample images, this paper proposed a method based on color space conversion, self-organizing feature map (SOFM) neural network and morphological operations. First, the conversion formula is built to convert RGB color space to CMYK color space. Second, according to the information of M channel in the CMYK color space, SOFM is used to reduce gray level and then the binary image can be got. Third, morphological operation is used to distinguish and count single bacterium, mass bacteria, and color drop. With abundant samples, experimental results indicate our method can extract the objects both accurately and availably.

AB - In the bacteria images automatic analysis system, bacteria detection is a vital step. According to the characteristics of sample images, this paper proposed a method based on color space conversion, self-organizing feature map (SOFM) neural network and morphological operations. First, the conversion formula is built to convert RGB color space to CMYK color space. Second, according to the information of M channel in the CMYK color space, SOFM is used to reduce gray level and then the binary image can be got. Third, morphological operation is used to distinguish and count single bacterium, mass bacteria, and color drop. With abundant samples, experimental results indicate our method can extract the objects both accurately and availably.

KW - Color space

KW - Morphological operation

KW - Self-organizing feature map (SOFM)

UR - http://www.scopus.com/inward/record.url?scp=47349117965&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=47349117965&partnerID=8YFLogxK

U2 - 10.1109/ICIA.2007.4295733

DO - 10.1109/ICIA.2007.4295733

M3 - Conference contribution

AN - SCOPUS:47349117965

SN - 1424412196

SN - 9781424412198

SP - 237

EP - 240

BT - Proceedings of the 2007 International Conference on Information Acquisition, ICIA

T2 - International Conference on Information Acquisition, ICIA 2007

Y2 - 9 July 2007 through 11 July 2007

ER -

Automatic detection method of bacteria

Abstract

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Keywords

ASJC Scopus subject areas

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