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Blood cell image segmentation using hybrid K-means and median-cut algorithms

In blood cell image analysis, segmentation is crucial step in quantitative cytophotometry. Blood cell images have become particularly useful in medical diagnostics tools for cases involving blood. In this paper, we present a better approach on merging segmentation algorithms of K-means and Median-cu...

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Main Authors: T.Z.T., Muda,, R.A., Salam,
Format: Conference Paper
Language:en_US
Published: 2015
Subjects:
Blood Cell Images
Fuzzy c-means
K-means
Means-shift
Median-cut
Segmentation
Online Access:http://ddms.usim.edu.my/handle/123456789/9049
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spelling my.usim-90492015-08-12T07:13:21Z Blood cell image segmentation using hybrid K-means and median-cut algorithms T.Z.T., Muda, R.A., Salam, Blood Cell Images Fuzzy c-means K-means Means-shift Median-cut Segmentation In blood cell image analysis, segmentation is crucial step in quantitative cytophotometry. Blood cell images have become particularly useful in medical diagnostics tools for cases involving blood. In this paper, we present a better approach on merging segmentation algorithms of K-means and Median-cut for colour blood cells images. Median-cut technique will be employed after comparing best outcomes from Fuzzy c-means, K-means and Means-shift. We used blood cell images infected with malaria parasites as cell images for our research. The result of proposed method shows better improvement in terms of object segmentations for further feature extraction process. © 2011 IEEE. 2015-08-12T07:13:21Z 2015-08-12T07:13:21Z 2011 Conference Paper 9781-4577-1642-3 http://ddms.usim.edu.my/handle/123456789/9049 en_US
institution Universiti Sains Islam Malaysia
building USIM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universit Sains Islam i Malaysia
content_source USIM Institutional Repository
url_provider http://ddms.usim.edu.my/
language en_US
topic Blood Cell Images
Fuzzy c-means
K-means
Means-shift
Median-cut
Segmentation
spellingShingle Blood Cell Images
Fuzzy c-means
K-means
Means-shift
Median-cut
Segmentation
T.Z.T., Muda,
R.A., Salam,
Blood cell image segmentation using hybrid K-means and median-cut algorithms
description In blood cell image analysis, segmentation is crucial step in quantitative cytophotometry. Blood cell images have become particularly useful in medical diagnostics tools for cases involving blood. In this paper, we present a better approach on merging segmentation algorithms of K-means and Median-cut for colour blood cells images. Median-cut technique will be employed after comparing best outcomes from Fuzzy c-means, K-means and Means-shift. We used blood cell images infected with malaria parasites as cell images for our research. The result of proposed method shows better improvement in terms of object segmentations for further feature extraction process. © 2011 IEEE.
format Conference Paper
author T.Z.T., Muda,
R.A., Salam,
author_facet T.Z.T., Muda,
R.A., Salam,
author_sort T.Z.T., Muda,
title Blood cell image segmentation using hybrid K-means and median-cut algorithms
title_short Blood cell image segmentation using hybrid K-means and median-cut algorithms
title_full Blood cell image segmentation using hybrid K-means and median-cut algorithms
title_fullStr Blood cell image segmentation using hybrid K-means and median-cut algorithms
title_full_unstemmed Blood cell image segmentation using hybrid K-means and median-cut algorithms
title_sort blood cell image segmentation using hybrid k-means and median-cut algorithms
publishDate 2015
url http://ddms.usim.edu.my/handle/123456789/9049
_version_ 1645152527272902656
score 13.214268

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