GA based optimisation of a multi-agent soft computing model for segmentation and classification of unstained mammalian cell images

C. Lai, R. Khosla, Yasue Mitsukura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Most existing approaches for determining serious pathological conditions involve analysis of stained images of human tissue. Recently, unstained methods have been used for classification and analysis of cells in human and mammalian tissues. The classifications accuracies have been have been quite poor. We describe a novel application of genetic algorithms for significantly improving the segmentation and classification of cells in unstained Chinese hamster ovarian image samples. The multiagent soft computing model represents a symbiotic relationship between soft computing agents like genetic algorithms, neural networks and water immersion and morphological agents for segmentation and classification of cells in unstained Chinese hamster ovarian image samples.

Original languageEnglish
Title of host publication2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings
PublisherIEEE Computer Society
Pages1192-1198
Number of pages7
Volume2
DOIs
Publication statusPublished - 2003
Externally publishedYes
Event2003 Congress on Evolutionary Computation, CEC 2003 - Canberra, ACT, Australia
Duration: 2003 Dec 82003 Dec 12

Other

Other2003 Congress on Evolutionary Computation, CEC 2003
CountryAustralia
CityCanberra, ACT
Period03/12/803/12/12

Fingerprint

Soft computing
Soft Computing
Segmentation
Cells
Optimization
Cell
Genetic algorithms
Genetic Algorithm
Tissue
Immersion
Model
Neural Networks
Neural networks
Water
Gas
Human

ASJC Scopus subject areas

  • Computational Mathematics

Cite this

Lai, C., Khosla, R., & Mitsukura, Y. (2003). GA based optimisation of a multi-agent soft computing model for segmentation and classification of unstained mammalian cell images. In 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings (Vol. 2, pp. 1192-1198). [1299804] IEEE Computer Society. https://doi.org/10.1109/CEC.2003.1299804

GA based optimisation of a multi-agent soft computing model for segmentation and classification of unstained mammalian cell images. / Lai, C.; Khosla, R.; Mitsukura, Yasue.

2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings. Vol. 2 IEEE Computer Society, 2003. p. 1192-1198 1299804.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lai, C, Khosla, R & Mitsukura, Y 2003, GA based optimisation of a multi-agent soft computing model for segmentation and classification of unstained mammalian cell images. in 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings. vol. 2, 1299804, IEEE Computer Society, pp. 1192-1198, 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, ACT, Australia, 03/12/8. https://doi.org/10.1109/CEC.2003.1299804
Lai C, Khosla R, Mitsukura Y. GA based optimisation of a multi-agent soft computing model for segmentation and classification of unstained mammalian cell images. In 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings. Vol. 2. IEEE Computer Society. 2003. p. 1192-1198. 1299804 https://doi.org/10.1109/CEC.2003.1299804
Lai, C. ; Khosla, R. ; Mitsukura, Yasue. / GA based optimisation of a multi-agent soft computing model for segmentation and classification of unstained mammalian cell images. 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings. Vol. 2 IEEE Computer Society, 2003. pp. 1192-1198
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