Comparative Analysis on Blood Cell Image Segmentation

DOI

10.2991/3ca-13.2013.115How to use a DOI?

Keywords

Segmentation, Blood Cell Images, Means-shift, Fuzzy c-means, K-means, Median-cut

Abstract

Image segmentation is an important phase in image recognition system. In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry.  Currently, blood cell images become predominantly valuable in medical diagnostics tools. In this paper, we present a comparative analysis on several segmentation algorithms. Three selected common approaches, that are Fuzzy c-means, K-means and Mean-shift were presented. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method that is K-means was selected. K-means has been enhanced by integrating Median-cut algorithm to further improve the segmentation process. The proposed integrated method has shown a significant improvement in the number of selected regions.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - T. Zalizam T. Muda
AU  - Rosalina Abdul Salam
PY  - 2013/04
DA  - 2013/04
TI  - Comparative Analysis on Blood Cell Image Segmentation
BT  - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation
PB  - Atlantis Press
SP  - 474
EP  - 477
SN  - 1951-6851
UR  - https://doi.org/10.2991/3ca-13.2013.115
DO  - 10.2991/3ca-13.2013.115
ID  - T.Muda2013/04
ER  -