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Blood cell image segmentation using unsupervised clustering techniques

Tuan Muda, Tuan Zalizam and Abdul Salam, Rosalina (2009) Blood cell image segmentation using unsupervised clustering techniques. In: International Conference on Computing and Informatics 2009 (ICOCI09) , 24-25 June 2009, Legend Hotel, Kuala Lumpur.

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In blood cell image analysis, segmentation is an indispensable step in quantitative cytophotometry. Blood cell images have become particularly useful in medical diagnostic tools for cases involving blood. The aim of our research is to develop an effective algorithm for segmentation of the blood cell images. In this paper, we present a framework of comparison cell images segmentation by using unsupervised clustering techniques with the purpose of acquiring the best method to segment the cell images. We use blood cell images infected with malaria parasites as cell images for our framework. Methods that involved in this comparison framework are Fuzzy C Means, K Means and Means Shift Analysis. The outcome from these methods will help to identify which technique or algorithm is the best for cell segmentation. Results from the segmented cell will be use for further classification and recognition.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN 978-983--44150-2-0; Organized by: UUM College of Arts and Sciences, Universiti Utara Malaysia.
Uncontrolled Keywords: Segmentation, Cell Images, Fuzzy C Means, K Means, Means Shift Analysis
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > R Medicine (General)
Divisions: College of Arts and Sciences
Depositing User: Mr. Tuan Zalizam Tuan Muda
Date Deposited: 28 Nov 2011 06:12
Last Modified: 02 Apr 2015 04:03
URI: http://repo.uum.edu.my/id/eprint/3713

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