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Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images

Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images

Savitha Balakrishnan, Subashini Parthasarathy, Krishnaveni Marimuthu
Copyright: © 2016 |Pages: 23
ISBN13: 9781466688117|ISBN10: 1466688114|EISBN13: 9781466688124
DOI: 10.4018/978-1-4666-8811-7.ch013
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MLA

Balakrishnan, Savitha, et al. "Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images." Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes, edited by Wahiba Ben Abdessalem Karâa and Nilanjan Dey, IGI Global, 2016, pp. 262-284. https://doi.org/10.4018/978-1-4666-8811-7.ch013

APA

Balakrishnan, S., Parthasarathy, S., & Marimuthu, K. (2016). Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images. In W. Karâa & N. Dey (Eds.), Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes (pp. 262-284). IGI Global. https://doi.org/10.4018/978-1-4666-8811-7.ch013

Chicago

Balakrishnan, Savitha, Subashini Parthasarathy, and Krishnaveni Marimuthu. "Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images." In Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes, edited by Wahiba Ben Abdessalem Karâa and Nilanjan Dey, 262-284. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-8811-7.ch013

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Abstract

Automated Segmentation of cell nuclei in Pap smear images plays an important role in the cervical cancer cell analysis systems to make a correct diagnosis decision. The aim of this chapter is to detail about the variety of computational intelligence and image processing approaches developed and used for the nuclei segmentation. In additional, the threshold based segmentation problem is treated as an optimization problem with an objective of preserving both the size and volume of the cell nuclei and also to segment the nuclei region from the original microscopic Pap smear image with the help of Particle Swarm Optimization (PSO) and Ant Colony Optimization techniques (ACO). Experimental results are shown, compared in quantitative and qualitative manner as well as the main advantages and limitations of each algorithm are explained.

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