Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images

View Sample PDF

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: 2017
Pages: 22
Source title: Biometrics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0983-7.ch012

Keywords: Biometrics / Information Science Reference / Medicine & Healthcare / Surveillance Systems

Purchase

View Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images on the publisher's website for pricing and purchasing information.

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.

The IRMA Community

Research IRM

Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images

Purchase

Abstract

Related Content

IRMA Sponsors