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Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images
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Author(s): Savitha Balakrishnan (Avinashilingam Institute for Home Science and Higher Education for Women University, India), Subashini Parthasarathy (Avinashilingam Institute for Home Science and Higher Education for Women University, India)and Krishnaveni Marimuthu (Avinashilingam Institute for Home Science and Higher Education for Women University, India)
Copyright: 2016
Pages: 23
Source title:
Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes
Source Author(s)/Editor(s): Wahiba Ben Abdessalem KarĂ¢a (Taif University, Saudi Arabia & RIADI-GDL Laboratory, ENSI, Tunisia)and Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India)
DOI: 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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