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Image segmentation using an adaptive clustering technique for the detection of acute leukemia blood cells images

Clustering is one of the most common automated image segmentation techniques used in many fields including machine learning, pattern recognition, image processing, and bioinformatics. Recently many scientists have performed tremendous research in helping the hematologists in the issue of segmenting...

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Main Authors: F.H.A., Jabar,, W., Ismail,, R.A., Salam,, R, Hassan,
Format: Conference Paper
Language:en_US
Published: IEEE Computer Society 2015
Subjects:
Acute leukemia cells
Clustering
Image segmentation
K-means
Mean shift
Online Access:http://ddms.usim.edu.my/handle/123456789/9204
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spelling my.usim-92042017-03-16T02:49:53Z Image segmentation using an adaptive clustering technique for the detection of acute leukemia blood cells images F.H.A., Jabar, W., Ismail, R.A., Salam, R, Hassan, Acute leukemia cells Clustering Image segmentation K-means Mean shift Clustering is one of the most common automated image segmentation techniques used in many fields including machine learning, pattern recognition, image processing, and bioinformatics. Recently many scientists have performed tremendous research in helping the hematologists in the issue of segmenting the blood cells in the early of prognosis. This paper aims to segment the blood cell images of patients suffering from acute leukemia using an adaptive K-Means clustering together with mean shift algorithm. The integrated clustering techniques have produced comprehensive output images with minimal filtering process to remove the background scene. © 2013 IEEE. 2015-08-25T04:56:14Z 2015-08-25T04:56:14Z 2014 Conference Paper 9781-4799-2758-6 http://ddms.usim.edu.my/handle/123456789/9204 en_US IEEE Computer Society
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 Acute leukemia cells
Clustering
Image segmentation
K-means
Mean shift
spellingShingle Acute leukemia cells
Clustering
Image segmentation
K-means
Mean shift
F.H.A., Jabar,
W., Ismail,
R.A., Salam,
R, Hassan,
Image segmentation using an adaptive clustering technique for the detection of acute leukemia blood cells images
description Clustering is one of the most common automated image segmentation techniques used in many fields including machine learning, pattern recognition, image processing, and bioinformatics. Recently many scientists have performed tremendous research in helping the hematologists in the issue of segmenting the blood cells in the early of prognosis. This paper aims to segment the blood cell images of patients suffering from acute leukemia using an adaptive K-Means clustering together with mean shift algorithm. The integrated clustering techniques have produced comprehensive output images with minimal filtering process to remove the background scene. © 2013 IEEE.
format Conference Paper
author F.H.A., Jabar,
W., Ismail,
R.A., Salam,
R, Hassan,
author_facet F.H.A., Jabar,
W., Ismail,
R.A., Salam,
R, Hassan,
author_sort F.H.A., Jabar,
title Image segmentation using an adaptive clustering technique for the detection of acute leukemia blood cells images
title_short Image segmentation using an adaptive clustering technique for the detection of acute leukemia blood cells images
title_full Image segmentation using an adaptive clustering technique for the detection of acute leukemia blood cells images
title_fullStr Image segmentation using an adaptive clustering technique for the detection of acute leukemia blood cells images
title_full_unstemmed Image segmentation using an adaptive clustering technique for the detection of acute leukemia blood cells images
title_sort image segmentation using an adaptive clustering technique for the detection of acute leukemia blood cells images
publisher IEEE Computer Society
publishDate 2015
url http://ddms.usim.edu.my/handle/123456789/9204
_version_ 1645152562037391360
score 13.222552

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