An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing

Image Segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consisting of two phases. In the first phase, the MRI brain image is acquired from patients database, In that film artifact and noise are removed. After that Hierarch...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	International Journal of Computer Theory and Engineering 2010-08, Vol.2 (4), p.586-590
Hauptverfasser:	Logeswari, T, Karnan, M
Format:	Artikel
Sprache:	eng
Schlagworte:	Brain Image segmentation Organizing Patients Segmentation Tumors Vector quantization
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page	590
container_issue	4
container_start_page	586
container_title	International Journal of Computer Theory and Engineering
container_volume	2
creator	Logeswari, T Karnan, M
description	Image Segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consisting of two phases. In the first phase, the MRI brain image is acquired from patients database, In that film artifact and noise are removed. After that Hierarchical Self Organizing Map (HSOM) is applied for image segmentation. The HSOM is the extension of the conventional self organizing map used to classify the image row by row. In this lowest level of weight vector, a higher value of tumor pixels, computation speed is achieved by the HSOM with vector quantization.
doi_str_mv	10.7763/IJCTE.2010.V2.206
format	Article
fullrecord	<record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1701046167</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1701046167</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2276-9cb519ed95e979e306ece5ef99324be8ea683a0d0e94c154784eaf2294af68883</originalsourceid><addsrcrecordid>eNqFkTtPwzAUhTOARFX6A9gssbCkxI_4MbahQFElhj5GLDe9KUGJXexk4N_jtkhILEzn3ns-XenoJMkNzsZCcHo_fylWszHJ4r4hUflFMsBC0VTG21UyCqHeRpMrgjkZJG8Ti2b23dgSdmjeHhpowXamq51FrkJTb2qLVn3rPHqADsqTsQ613aMl7H_ZqQnxQRyWrupQ4dpD30XoOrmsTBNg9KPDZP04WxXP6eL1aV5MFmlJiOCpKrc5VrBTOSihgGYcSsihUooStgUJhktqsl0GipU4Z0IyMBUhipmKSynpMLk7_z1499lD6HRbhxKaxlhwfdBYxMyMYy7-R3NMmeQS44je_kE_XO9tDKIxY1kkOc4ihc9U6V0IHip98HVr_JfGmT52ok-d6GMnekOicvoN2DWAIA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1440151610</pqid></control><display><type>article</type><title>An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>Logeswari, T ; Karnan, M</creator><creatorcontrib>Logeswari, T ; Karnan, M</creatorcontrib><description>Image Segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consisting of two phases. In the first phase, the MRI brain image is acquired from patients database, In that film artifact and noise are removed. After that Hierarchical Self Organizing Map (HSOM) is applied for image segmentation. The HSOM is the extension of the conventional self organizing map used to classify the image row by row. In this lowest level of weight vector, a higher value of tumor pixels, computation speed is achieved by the HSOM with vector quantization.</description><identifier>ISSN: 1793-8201</identifier><identifier>DOI: 10.7763/IJCTE.2010.V2.206</identifier><language>eng</language><publisher>Singapore: IACSIT Press</publisher><subject>Brain ; Image segmentation ; Organizing ; Patients ; Segmentation ; Tumors ; Vector quantization</subject><ispartof>International Journal of Computer Theory and Engineering, 2010-08, Vol.2 (4), p.586-590</ispartof><rights>Copyright IACSIT Press Aug 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2276-9cb519ed95e979e306ece5ef99324be8ea683a0d0e94c154784eaf2294af68883</citedby><cites>FETCH-LOGICAL-c2276-9cb519ed95e979e306ece5ef99324be8ea683a0d0e94c154784eaf2294af68883</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Logeswari, T</creatorcontrib><creatorcontrib>Karnan, M</creatorcontrib><title>An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing</title><title>International Journal of Computer Theory and Engineering</title><description>Image Segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consisting of two phases. In the first phase, the MRI brain image is acquired from patients database, In that film artifact and noise are removed. After that Hierarchical Self Organizing Map (HSOM) is applied for image segmentation. The HSOM is the extension of the conventional self organizing map used to classify the image row by row. In this lowest level of weight vector, a higher value of tumor pixels, computation speed is achieved by the HSOM with vector quantization.</description><subject>Brain</subject><subject>Image segmentation</subject><subject>Organizing</subject><subject>Patients</subject><subject>Segmentation</subject><subject>Tumors</subject><subject>Vector quantization</subject><issn>1793-8201</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNqFkTtPwzAUhTOARFX6A9gssbCkxI_4MbahQFElhj5GLDe9KUGJXexk4N_jtkhILEzn3ns-XenoJMkNzsZCcHo_fylWszHJ4r4hUflFMsBC0VTG21UyCqHeRpMrgjkZJG8Ti2b23dgSdmjeHhpowXamq51FrkJTb2qLVn3rPHqADsqTsQ613aMl7H_ZqQnxQRyWrupQ4dpD30XoOrmsTBNg9KPDZP04WxXP6eL1aV5MFmlJiOCpKrc5VrBTOSihgGYcSsihUooStgUJhktqsl0GipU4Z0IyMBUhipmKSynpMLk7_z1499lD6HRbhxKaxlhwfdBYxMyMYy7-R3NMmeQS44je_kE_XO9tDKIxY1kkOc4ihc9U6V0IHip98HVr_JfGmT52ok-d6GMnekOicvoN2DWAIA</recordid><startdate>20100801</startdate><enddate>20100801</enddate><creator>Logeswari, T</creator><creator>Karnan, M</creator><general>IACSIT Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20100801</creationdate><title>An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing</title><author>Logeswari, T ; Karnan, M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2276-9cb519ed95e979e306ece5ef99324be8ea683a0d0e94c154784eaf2294af68883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Brain</topic><topic>Image segmentation</topic><topic>Organizing</topic><topic>Patients</topic><topic>Segmentation</topic><topic>Tumors</topic><topic>Vector quantization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Logeswari, T</creatorcontrib><creatorcontrib>Karnan, M</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International Journal of Computer Theory and Engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Logeswari, T</au><au>Karnan, M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing</atitle><jtitle>International Journal of Computer Theory and Engineering</jtitle><date>2010-08-01</date><risdate>2010</risdate><volume>2</volume><issue>4</issue><spage>586</spage><epage>590</epage><pages>586-590</pages><issn>1793-8201</issn><abstract>Image Segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consisting of two phases. In the first phase, the MRI brain image is acquired from patients database, In that film artifact and noise are removed. After that Hierarchical Self Organizing Map (HSOM) is applied for image segmentation. The HSOM is the extension of the conventional self organizing map used to classify the image row by row. In this lowest level of weight vector, a higher value of tumor pixels, computation speed is achieved by the HSOM with vector quantization.</abstract><cop>Singapore</cop><pub>IACSIT Press</pub><doi>10.7763/IJCTE.2010.V2.206</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record>
fulltext	fulltext
identifier	ISSN: 1793-8201
ispartof	International Journal of Computer Theory and Engineering, 2010-08, Vol.2 (4), p.586-590
issn	1793-8201
language	eng
recordid	cdi_proquest_miscellaneous_1701046167
source	EZB-FREE-00999 freely available EZB journals
subjects	Brain Image segmentation Organizing Patients Segmentation Tumors Vector quantization
title	An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T06%3A02%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20Enhanced%20Implementation%20of%20Brain%20Tumor%20Detection%20Using%20Segmentation%20Based%20on%20Soft%20Computing&rft.jtitle=International%20Journal%20of%20Computer%20Theory%20and%20Engineering&rft.au=Logeswari,%20T&rft.date=2010-08-01&rft.volume=2&rft.issue=4&rft.spage=586&rft.epage=590&rft.pages=586-590&rft.issn=1793-8201&rft_id=info:doi/10.7763/IJCTE.2010.V2.206&rft_dat=%3Cproquest_cross%3E1701046167%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1440151610&rft_id=info:pmid/&rfr_iscdi=true