Glioblastoma multiforme identification from medical imaging using computer vision

A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at...

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Main Authors:	Mohd. Azhari, Ed-Edily, Mohd. Hatta, Muhd. Mudzakkir, Htike@Muhammad Yusof, Zaw Zaw, Shoon , Lei Win
Format:	Article
Language:	English
Published:	Wireilla Scientific Publications, Australia 2014
Subjects:	Q Science (General)
Online Access:	http://irep.iium.edu.my/38134/ http://irep.iium.edu.my/38134/ http://irep.iium.edu.my/38134/ http://irep.iium.edu.my/38134/1/3214ijscmc01.pdf

id	iium-38134
recordtype	eprints
spelling	iium-381342018-06-19T08:40:10Z http://irep.iium.edu.my/38134/ Glioblastoma multiforme identification from medical imaging using computer vision Mohd. Azhari, Ed-Edily Mohd. Hatta, Muhd. Mudzakkir Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win Q Science (General) A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. We propose an automatic brain tumor detection and localization framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge detection, modified histogram clustering and morphological operations. After morphological operations, tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to extend this framework to detect and localize a variety of other types of tumors in other types of medical imagery. Wireilla Scientific Publications, Australia 2014-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/38134/1/3214ijscmc01.pdf Mohd. Azhari, Ed-Edily and Mohd. Hatta, Muhd. Mudzakkir and Htike@Muhammad Yusof, Zaw Zaw and Shoon , Lei Win (2014) Glioblastoma multiforme identification from medical imaging using computer vision. International Journal of Soft Computing, Mathematics and Control (IJSCMC), 3 (2). pp. 1-12. ISSN 2201-4160 http://wireilla.com/ns/maths/current2014.html 10.14810/ijscmc.2014.3201
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Mohd. Azhari, Ed-Edily Mohd. Hatta, Muhd. Mudzakkir Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win Glioblastoma multiforme identification from medical imaging using computer vision
description	A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. We propose an automatic brain tumor detection and localization framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge detection, modified histogram clustering and morphological operations. After morphological operations, tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to extend this framework to detect and localize a variety of other types of tumors in other types of medical imagery.
format	Article
author	Mohd. Azhari, Ed-Edily Mohd. Hatta, Muhd. Mudzakkir Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win
author_facet	Mohd. Azhari, Ed-Edily Mohd. Hatta, Muhd. Mudzakkir Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win
author_sort	Mohd. Azhari, Ed-Edily
title	Glioblastoma multiforme identification from medical imaging using computer vision
title_short	Glioblastoma multiforme identification from medical imaging using computer vision
title_full	Glioblastoma multiforme identification from medical imaging using computer vision
title_fullStr	Glioblastoma multiforme identification from medical imaging using computer vision
title_full_unstemmed	Glioblastoma multiforme identification from medical imaging using computer vision
title_sort	glioblastoma multiforme identification from medical imaging using computer vision
publisher	Wireilla Scientific Publications, Australia
publishDate	2014
url	http://irep.iium.edu.my/38134/ http://irep.iium.edu.my/38134/ http://irep.iium.edu.my/38134/ http://irep.iium.edu.my/38134/1/3214ijscmc01.pdf
first_indexed	2023-09-18T20:54:44Z
last_indexed	2023-09-18T20:54:44Z
_version_	1777410228323614720

Glioblastoma multiforme identification from medical imaging using computer vision

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