FPGA implementation on MRI brain classification using support vector machine

The field of medical imaging gains its importance with in crease in the need of automated and efficient diagnosis in a short period of time. Brain images have been selected for the image references since injuries to the brain tend to affect other organs. Magnetic Resonance Imaging (MRI) is an imagin...

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Main Author:	Abdullah, Noramalina
Format:	Thesis
Language:	English
Published:	2009
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/12307/6/NoramalinaAbdullahMFKE2009.pdf
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id	my-utm-ep.12307
record_format	uketd_dc
spelling	my-utm-ep.123072017-09-14T03:31:43Z FPGA implementation on MRI brain classification using support vector machine 2009-11 Abdullah, Noramalina TK Electrical engineering. Electronics Nuclear engineering The field of medical imaging gains its importance with in crease in the need of automated and efficient diagnosis in a short period of time. Brain images have been selected for the image references since injuries to the brain tend to affect other organs. Magnetic Resonance Imaging (MRI) is an imaging technique that has been playing an important role in neuroscience research for studying brain images. The classifications of brain MRI data as normal and abnormal are important to prune the normal patient and to consider only those who have the possibility of having abnormalities or tumor. An advanced kernel based techniques such as Support Vector Machine (SVM) for the classification of volume of MRI data as normal and abnormal will be deployed. Image processing tasks are computationally intensive due to the vast amount of data that requires the processing of more than seven million pixels per second for typical images sources. To keep up with this, a careful and creative data management must be provided. Field Programmable Gate Array (FPGA) is one of the alternatives that offer custom computing platform, sufficiently flexible and fast enough for new algorithms to be implemented on existing hardware. 2009-11 Thesis http://eprints.utm.my/id/eprint/12307/ http://eprints.utm.my/id/eprint/12307/6/NoramalinaAbdullahMFKE2009.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 Abdullah, Noramalina FPGA implementation on MRI brain classification using support vector machine
description	The field of medical imaging gains its importance with in crease in the need of automated and efficient diagnosis in a short period of time. Brain images have been selected for the image references since injuries to the brain tend to affect other organs. Magnetic Resonance Imaging (MRI) is an imaging technique that has been playing an important role in neuroscience research for studying brain images. The classifications of brain MRI data as normal and abnormal are important to prune the normal patient and to consider only those who have the possibility of having abnormalities or tumor. An advanced kernel based techniques such as Support Vector Machine (SVM) for the classification of volume of MRI data as normal and abnormal will be deployed. Image processing tasks are computationally intensive due to the vast amount of data that requires the processing of more than seven million pixels per second for typical images sources. To keep up with this, a careful and creative data management must be provided. Field Programmable Gate Array (FPGA) is one of the alternatives that offer custom computing platform, sufficiently flexible and fast enough for new algorithms to be implemented on existing hardware.
format	Thesis
qualification_level	Master's degree
author	Abdullah, Noramalina
author_facet	Abdullah, Noramalina
author_sort	Abdullah, Noramalina
title	FPGA implementation on MRI brain classification using support vector machine
title_short	FPGA implementation on MRI brain classification using support vector machine
title_full	FPGA implementation on MRI brain classification using support vector machine
title_fullStr	FPGA implementation on MRI brain classification using support vector machine
title_full_unstemmed	FPGA implementation on MRI brain classification using support vector machine
title_sort	fpga implementation on mri brain classification using support vector machine
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2009
url	http://eprints.utm.my/id/eprint/12307/6/NoramalinaAbdullahMFKE2009.pdf
_version_	1747814919446724608

FPGA implementation on MRI brain classification using support vector machine

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