Learning tools for blood dell segmentation and extraction techniques

Blood cell segmentation and identification is vital in the study of blood as a health indicator. A complete blood count is used to determine the state of a person’s health based on the contents of the blood in particular the white blood cells and the red blood cells. The main problem arises when mas...

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Main Author:	Poon, Chee Lim
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/33211/1/PoonCheeLimMFKE2013.pdf
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id	my-utm-ep.33211
record_format	uketd_dc
spelling	my-utm-ep.332112017-09-14T07:01:22Z Learning tools for blood dell segmentation and extraction techniques 2013-01 Poon, Chee Lim TK Electrical engineering. Electronics Nuclear engineering Blood cell segmentation and identification is vital in the study of blood as a health indicator. A complete blood count is used to determine the state of a person’s health based on the contents of the blood in particular the white blood cells and the red blood cells. The main problem arises when massive amounts of blood samples are required to be processed by the haematologist or Medical Laboratory Technicians. The time and skill required for the task limits the speed and accuracy with which the blood sample can be processed. This project aims to provide user-friendly software based on MATLAB allowing for quick user interaction with a simple tool for the segmentation and identification of red and white blood cells from a provided image. The project presents the solution in a modular framework allowing for future development within a structured environment. In order to perform the segmentation, this project uses k-means clustering and colour based segmentation using International Commission on Illumination Lab* (CIELAB) colour space coupled with polygon information of the region of interest. The project integrates these methods into a flow within a Graphical User Interface (GUI) with customizable variables to handle changing input images. The result of the project is a working GUI with the capability to accept user interaction. The completed project is able to obtain quick and accurate blood cell segmentation of both red and white blood cells. The accuracy of this project ranges from 64% to 87% depending on the type of processing used and the type of cells being extracted. 2013-01 Thesis http://eprints.utm.my/id/eprint/33211/ http://eprints.utm.my/id/eprint/33211/1/PoonCheeLimMFKE2013.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Poon, Chee Lim Learning tools for blood dell segmentation and extraction techniques
description	Blood cell segmentation and identification is vital in the study of blood as a health indicator. A complete blood count is used to determine the state of a person’s health based on the contents of the blood in particular the white blood cells and the red blood cells. The main problem arises when massive amounts of blood samples are required to be processed by the haematologist or Medical Laboratory Technicians. The time and skill required for the task limits the speed and accuracy with which the blood sample can be processed. This project aims to provide user-friendly software based on MATLAB allowing for quick user interaction with a simple tool for the segmentation and identification of red and white blood cells from a provided image. The project presents the solution in a modular framework allowing for future development within a structured environment. In order to perform the segmentation, this project uses k-means clustering and colour based segmentation using International Commission on Illumination Lab* (CIELAB) colour space coupled with polygon information of the region of interest. The project integrates these methods into a flow within a Graphical User Interface (GUI) with customizable variables to handle changing input images. The result of the project is a working GUI with the capability to accept user interaction. The completed project is able to obtain quick and accurate blood cell segmentation of both red and white blood cells. The accuracy of this project ranges from 64% to 87% depending on the type of processing used and the type of cells being extracted.
format	Thesis
qualification_level	Master's degree
author	Poon, Chee Lim
author_facet	Poon, Chee Lim
author_sort	Poon, Chee Lim
title	Learning tools for blood dell segmentation and extraction techniques
title_short	Learning tools for blood dell segmentation and extraction techniques
title_full	Learning tools for blood dell segmentation and extraction techniques
title_fullStr	Learning tools for blood dell segmentation and extraction techniques
title_full_unstemmed	Learning tools for blood dell segmentation and extraction techniques
title_sort	learning tools for blood dell segmentation and extraction techniques
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/33211/1/PoonCheeLimMFKE2013.pdf
_version_	1747816106265935872

Learning tools for blood dell segmentation and extraction techniques

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