Brain abnormality segmentation using k-Nearest Neighbour (k-NN) / Puteri Nurain Megat Mohd Haniff

This study uses k-Nearest Neighbour (k-NN) in segmentation of brain abnormality. Segmentation is a process of partitioning a digital image into multiple regions or set of pixels. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningf...

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Main Author:	Megat Mohd Haniff, Puteri Nurain
Format:	Thesis
Language:	English
Published:	2010
Online Access:	https://ir.uitm.edu.my/id/eprint/63667/1/63667.pdf
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id	my-uitm-ir.63667
record_format	uketd_dc
spelling	my-uitm-ir.636672024-03-26T01:20:41Z Brain abnormality segmentation using k-Nearest Neighbour (k-NN) / Puteri Nurain Megat Mohd Haniff 2010 Megat Mohd Haniff, Puteri Nurain This study uses k-Nearest Neighbour (k-NN) in segmentation of brain abnormality. Segmentation is a process of partitioning a digital image into multiple regions or set of pixels. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Segmentation of MRI image is an important part of brain imaging research. In this study, k-NN segmentation uses 150 images as testing data to test this prototype. These data are designed by cutting various shapes and size of various abnormalities and pasting it onto normal brain size which are has various category of background such as low, medium and high background gray level value. The experimental results show the good segmentation for medium and low background grey level value for light abnormality. Dark abnormality for each type of background also produced good segmentation. However, high background gray level value for light abnormality produced poor segmentation because texture of background and light abnormality seen like same. In the future, this project needs to use other techniques to produce accuracy result for light and dark abnormality even k-NN segmentation produced good segmentation. 2010 Thesis https://ir.uitm.edu.my/id/eprint/63667/ https://ir.uitm.edu.my/id/eprint/63667/1/63667.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Abd. Khalid, Noor Elaiza
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Abd. Khalid, Noor Elaiza
description	This study uses k-Nearest Neighbour (k-NN) in segmentation of brain abnormality. Segmentation is a process of partitioning a digital image into multiple regions or set of pixels. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Segmentation of MRI image is an important part of brain imaging research. In this study, k-NN segmentation uses 150 images as testing data to test this prototype. These data are designed by cutting various shapes and size of various abnormalities and pasting it onto normal brain size which are has various category of background such as low, medium and high background gray level value. The experimental results show the good segmentation for medium and low background grey level value for light abnormality. Dark abnormality for each type of background also produced good segmentation. However, high background gray level value for light abnormality produced poor segmentation because texture of background and light abnormality seen like same. In the future, this project needs to use other techniques to produce accuracy result for light and dark abnormality even k-NN segmentation produced good segmentation.
format	Thesis
qualification_level	Bachelor degree
author	Megat Mohd Haniff, Puteri Nurain
spellingShingle	Megat Mohd Haniff, Puteri Nurain Brain abnormality segmentation using k-Nearest Neighbour (k-NN) / Puteri Nurain Megat Mohd Haniff
author_facet	Megat Mohd Haniff, Puteri Nurain
author_sort	Megat Mohd Haniff, Puteri Nurain
title	Brain abnormality segmentation using k-Nearest Neighbour (k-NN) / Puteri Nurain Megat Mohd Haniff
title_short	Brain abnormality segmentation using k-Nearest Neighbour (k-NN) / Puteri Nurain Megat Mohd Haniff
title_full	Brain abnormality segmentation using k-Nearest Neighbour (k-NN) / Puteri Nurain Megat Mohd Haniff
title_fullStr	Brain abnormality segmentation using k-Nearest Neighbour (k-NN) / Puteri Nurain Megat Mohd Haniff
title_full_unstemmed	Brain abnormality segmentation using k-Nearest Neighbour (k-NN) / Puteri Nurain Megat Mohd Haniff
title_sort	brain abnormality segmentation using k-nearest neighbour (k-nn) / puteri nurain megat mohd haniff
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2010
url	https://ir.uitm.edu.my/id/eprint/63667/1/63667.pdf
_version_	1804889653313011712

Brain abnormality segmentation using k-Nearest Neighbour (k-NN) / Puteri Nurain Megat Mohd Haniff

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