Image Recognition and Counting for the Bacilli Cell Based on the Microscopic Image

Min Tan; Ji Kang Zhong; Guo Zhao Zhang; Zhi Xiang Hu

doi:10.4028/www.scientific.net/AMM.651-653.2318

Paper Titles

Effect Evaluation Method of Video Surveillance System
p.2301

Face Tracking Based on Particle Filtering and α-β-γ Filtering
p.2306

Functional Structure Modeling Based on Fuzzy Inference
p.2310

Identification of Hammerstein Systems with Two-Segment Preload Nonlinearity Based on the Gradient Search
p.2314

Image Recognition and Counting for the Bacilli Cell Based on the Microscopic Image
p.2318

Improved Chaotic Bacteria Foraging Optimization Algorithm Particle
p.2322

Improved Natural Gradient Algorithms for Multi-Channel Signal Separation
p.2326

Linear Feature Detectionin Images
p.2331

Measured Scan Data Reduction Combination Algorithm Based on Scan Line
p.2335

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 651-653Image Recognition and Counting for the Bacilli...

Image Recognition and Counting for the Bacilli Cell Based on the Microscopic Image

Abstract:

In order to automatically detect bacilli in sputum image with microscopy, an intelligent recognition method based on machine vision is presented. Firstly, a novel background filter was designed based on the single layer perceptron to realize object segmentation from background. After eliminating the short twig and small area noise, the suspicious goals and the image noise are separated. In the feature extraction, besides the base features of single bacillus two important features are presented to solve the difficult problem of identification and counting for the overlapping and winding bacilli cells. Finally, an EBP neural network classifier is designed for the accurate identification and counting of the bacilli cells. Experimental results verified the effectiveness of the presented method.

You might also be interested in these eBooks

Material Science, Civil Engineering and Architecture Science, Mechanical Engineering and Manufacturing Technology II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 651-653)

Pages:

2318-2321

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.651-653.2318

Citation:

Cite this paper

Online since:

September 2014

Authors:

Min Tan*, Ji Kang Zhong, Guo Zhao Zhang, Zhi Xiang Hu

Keywords:

Cell Overlapping, EBP Classifier, Intelligent Identification, Single Layer Perceptron

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] K. Veropoulos, G. Learmonth, C. Campbell, B. Knight, J. Simpson, Automatic identification of tubercle bacilli in sputum. A preliminary investigation, Analytical and Quantitative Cytology and Histology, 21(4), 277–81 (1999).

Google Scholar

[2] M. G. Forero, F. Sroubek, G. Cristóbal, Identification of tuberculosis bacteria based on shape and color, Real-Time Imaging. 10, 251–262 (2004).

DOI: 10.1016/j.rti.2004.05.007

Google Scholar

[3] M. Wilkinson, Rapid automatic segmentation of fluorescent and phase-contrast images of bacteria. In: Slavik J, editor. Fluores-cence microscopy and fluorescent probes, New York, NY: Plenum Press (1996).

DOI: 10.1007/978-1-4899-1866-6_40

Google Scholar

[4] M. G. Forero, G. Cristóbal and M. Desco, Automatic identification of Mycobacterium tuberculosis by Gaussian mixture models, Journal of Microscopy. 223, 120–132 (2006).

DOI: 10.1111/j.1365-2818.2006.01610.x

Google Scholar

[5] J. Mitchell, A Geometric Interpretation of Hidden Layer Units in Feedforward Neural Networks, Neural Networks. 3, 19- 25 (1992).

DOI: 10.1088/0954-898x_3_1_004

Google Scholar