Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia

Overlapping cell identification and classification of microscopic blood cell image is proposed to increase the accuracy of the number of automated counting and the percentage of the overall accuracy. The accurate identification of overlapping cells can increase the accuracy of cell counting for dia...

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Main Author:	Kiu, Siew Ming
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/26606/1/Kiu%20Siew%20ft.pdf
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id	my-unimas-ir.26606
record_format	uketd_dc
spelling	my-unimas-ir.266062024-02-20T04:59:42Z Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia 2018 Kiu, Siew Ming QA75 Electronic computers. Computer science Overlapping cell identification and classification of microscopic blood cell image is proposed to increase the accuracy of the number of automated counting and the percentage of the overall accuracy. The accurate identification of overlapping cells can increase the accuracy of cell counting for diagnosing Leukaemia disease. In the proposed method, the percentage of average accuracy for identifying overlapping cells had reached 98%. The overlapping cells had classified into different classes based on overlapping degree and the number of overlapping cells. The proposed method can successful segmentation white blood cell from the overlapping cells with 70%. The average percentage of cell counting had tested by the proposed method. Extended-Minima Transform Watershed Segmentation Algorithm had successful increasing 36.89% of accuracy to WBC segmentation. It had reduced 46.12% of over-segmentation problem. Tests of cell counting in between the two stages, which are before and after for the process of identification and classification of overlapping cells. The average percentage of total cell count is 83.31%, the average percentage of WBC counting is 84.8% and the average percentage of RBC counting is 60.55%. Universiti Malaysia Sarawak (UNIMAS) 2018 Thesis http://ir.unimas.my/id/eprint/26606/ http://ir.unimas.my/id/eprint/26606/1/Kiu%20Siew%20ft.pdf text en validuser masters Universiti Malaysia Sarawak (UNIMAS) Faculty of Computer Science and Information Technology
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Kiu, Siew Ming Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia
description	Overlapping cell identification and classification of microscopic blood cell image is proposed to increase the accuracy of the number of automated counting and the percentage of the overall accuracy. The accurate identification of overlapping cells can increase the accuracy of cell counting for diagnosing Leukaemia disease. In the proposed method, the percentage of average accuracy for identifying overlapping cells had reached 98%. The overlapping cells had classified into different classes based on overlapping degree and the number of overlapping cells. The proposed method can successful segmentation white blood cell from the overlapping cells with 70%. The average percentage of cell counting had tested by the proposed method. Extended-Minima Transform Watershed Segmentation Algorithm had successful increasing 36.89% of accuracy to WBC segmentation. It had reduced 46.12% of over-segmentation problem. Tests of cell counting in between the two stages, which are before and after for the process of identification and classification of overlapping cells. The average percentage of total cell count is 83.31%, the average percentage of WBC counting is 84.8% and the average percentage of RBC counting is 60.55%.
format	Thesis
qualification_level	Master's degree
author	Kiu, Siew Ming
author_facet	Kiu, Siew Ming
author_sort	Kiu, Siew Ming
title	Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia
title_short	Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia
title_full	Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia
title_fullStr	Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia
title_full_unstemmed	Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia
title_sort	geometric feature extraction for identification and classification of the overlapping cells for leukaemia
granting_institution	Universiti Malaysia Sarawak (UNIMAS)
granting_department	Faculty of Computer Science and Information Technology
publishDate	2018
url	http://ir.unimas.my/id/eprint/26606/1/Kiu%20Siew%20ft.pdf
_version_	1794023002185138176

Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia

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