Tumor detection based on enhanced hill climbing method

Image segmentation is good way to analyze information in various fields of life. Image processing, especially image segmentation is very important and beneficial especially for the medical image segmentation and many other fields, from the application of segmentation know how the segmentation is imp...

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主要作者:	Yaseen, Ali Taha
格式:	Thesis
语言:	English
出版:	2010
主题:	QA75 Electronic computers Computer science RZ Other systems of medicine
在线阅读:	http://eprints.utm.my/id/eprint/11058/6/AliTahaYaseenMFSKSM2010.pdf
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id	my-utm-ep.11058
record_format	uketd_dc
spelling	my-utm-ep.110582017-09-26T04:58:09Z Tumor detection based on enhanced hill climbing method 2010-04 Yaseen, Ali Taha QA75 Electronic computers. Computer science RZ Other systems of medicine Image segmentation is good way to analyze information in various fields of life. Image processing, especially image segmentation is very important and beneficial especially for the medical image segmentation and many other fields, from the application of segmentation know how the segmentation is important in our life. Image segmentation is the process of partitioning a digital image into sets of pixels. The aim of recognition system is to automatically identify the brain and extract the tumor from it. Several approaches have been proposed for medical segmentation. Some of the methods use color and brightness to reduce the complexity of the problem. Although such approaches solve the detect edges of regions. They are not able to handle almost any variation on the brain physical structure. There are many techniques have been proposed for tumor brain segmentation and Hill climbing is one of these techniques. The combination of the different approaches for the segmentation of brain images is presented in this project. Propose a color-based segmentation method that uses the K-means clustering technique with Hill climbing method to track tumor objects in magnetic resonance (MR) brain images, K-means clustering is used to cluster the image from gray to RGB scale while Hill climbing has been applied for the segmentation after that to overcome the problem with empty holes and Incoherent borders in the image , this project can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region. 2010-04 Thesis http://eprints.utm.my/id/eprint/11058/ http://eprints.utm.my/id/eprint/11058/6/AliTahaYaseenMFSKSM2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science RZ Other systems of medicine
spellingShingle	QA75 Electronic computers Computer science RZ Other systems of medicine Yaseen, Ali Taha Tumor detection based on enhanced hill climbing method
description	Image segmentation is good way to analyze information in various fields of life. Image processing, especially image segmentation is very important and beneficial especially for the medical image segmentation and many other fields, from the application of segmentation know how the segmentation is important in our life. Image segmentation is the process of partitioning a digital image into sets of pixels. The aim of recognition system is to automatically identify the brain and extract the tumor from it. Several approaches have been proposed for medical segmentation. Some of the methods use color and brightness to reduce the complexity of the problem. Although such approaches solve the detect edges of regions. They are not able to handle almost any variation on the brain physical structure. There are many techniques have been proposed for tumor brain segmentation and Hill climbing is one of these techniques. The combination of the different approaches for the segmentation of brain images is presented in this project. Propose a color-based segmentation method that uses the K-means clustering technique with Hill climbing method to track tumor objects in magnetic resonance (MR) brain images, K-means clustering is used to cluster the image from gray to RGB scale while Hill climbing has been applied for the segmentation after that to overcome the problem with empty holes and Incoherent borders in the image , this project can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region.
format	Thesis
qualification_level	Master's degree
author	Yaseen, Ali Taha
author_facet	Yaseen, Ali Taha
author_sort	Yaseen, Ali Taha
title	Tumor detection based on enhanced hill climbing method
title_short	Tumor detection based on enhanced hill climbing method
title_full	Tumor detection based on enhanced hill climbing method
title_fullStr	Tumor detection based on enhanced hill climbing method
title_full_unstemmed	Tumor detection based on enhanced hill climbing method
title_sort	tumor detection based on enhanced hill climbing method
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2010
url	http://eprints.utm.my/id/eprint/11058/6/AliTahaYaseenMFSKSM2010.pdf
_version_	1747814802799984640

Tumor detection based on enhanced hill climbing method

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