Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images
We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem. Our approach uses a metaheuristic search method called Harmony Search (HS) algorithm to produce near-optimal initial cluster centers for the FCM algorithm. We then demonstrate the effectiveness of our appr...
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| creator | Alia, O.M. Mandava, R. Ramachandram, D. Aziz, M.E. |
| description | We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem. Our approach uses a metaheuristic search method called Harmony Search (HS) algorithm to produce near-optimal initial cluster centers for the FCM algorithm. We then demonstrate the effectiveness of our approach in a MRI segmentation problem. In order to dramatically reduce the computation time to find near-optimal cluster centers, we use an alternate representation of the search space. Our experiments indicate encouraging results in producing stable clustering for the given problem as compared to using an FCM with randomly initialized cluster centers. |
| doi_str_mv | 10.1109/TENCON.2009.5396049 |
| format | Conference Proceeding |
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Our experiments indicate encouraging results in producing stable clustering for the given problem as compared to using an FCM with randomly initialized cluster centers.</description><subject>Biomedical imaging</subject><subject>Clustering algorithms</subject><subject>Digital images</subject><subject>Evolutionary computation</subject><subject>Image analysis</subject><subject>Image segmentation</subject><subject>Magnetic resonance imaging</subject><subject>Radiology</subject><subject>Search methods</subject><subject>Space exploration</subject><issn>2159-3442</issn><issn>2159-3450</issn><isbn>9781424445462</isbn><isbn>1424445469</isbn><isbn>1424445477</isbn><isbn>9781424445479</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9kM1uwjAQhN0fpALlCbj4BUL9s7bjY4VoqURBqqh6RE6ypq5IUsXhQJ6-qaCdyx4-zezuEDLlbMY5sw_bxXq-Wc8EY3ampNUM7BUZcRAAoMCYazIUXNlEgmI3ZGJN-se0uP1nIAZk9JthmbQM7sgkxi_WSzHDrBiSj6Vryro60YiuyT-TzEUsaH44xhYbGqrQBncInWtDXVFfN9Qfu-5E86REV8XetS-xas-49vT1jYbS7THek4F3h4iTyxyT96fFdr5MVpvnl_njKglc9hcqAUZpCVmRCeUNz0XqVc4LyQrFs1Qa4QBVJr3rX_BG60JmBUfNPHJtU5BjMj3nBkTcfTf99ua0uxQmfwAtT1kE</recordid><startdate>200911</startdate><enddate>200911</enddate><creator>Alia, O.M.</creator><creator>Mandava, R.</creator><creator>Ramachandram, D.</creator><creator>Aziz, M.E.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200911</creationdate><title>Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images</title><author>Alia, O.M. ; Mandava, R. ; Ramachandram, D. ; Aziz, M.E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i1359-52475634bdb25f71c28f5c1d30d51b8372a4e5b3fa099f766d3bd1e60fe169843</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Biomedical imaging</topic><topic>Clustering algorithms</topic><topic>Digital images</topic><topic>Evolutionary computation</topic><topic>Image analysis</topic><topic>Image segmentation</topic><topic>Magnetic resonance imaging</topic><topic>Radiology</topic><topic>Search methods</topic><topic>Space exploration</topic><toplevel>online_resources</toplevel><creatorcontrib>Alia, O.M.</creatorcontrib><creatorcontrib>Mandava, R.</creatorcontrib><creatorcontrib>Ramachandram, D.</creatorcontrib><creatorcontrib>Aziz, M.E.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Alia, O.M.</au><au>Mandava, R.</au><au>Ramachandram, D.</au><au>Aziz, M.E.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images</atitle><btitle>TENCON 2009 - 2009 IEEE Region 10 Conference</btitle><stitle>TENCON</stitle><date>2009-11</date><risdate>2009</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><issn>2159-3442</issn><eissn>2159-3450</eissn><isbn>9781424445462</isbn><isbn>1424445469</isbn><eisbn>1424445477</eisbn><eisbn>9781424445479</eisbn><abstract>We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem. Our approach uses a metaheuristic search method called Harmony Search (HS) algorithm to produce near-optimal initial cluster centers for the FCM algorithm. We then demonstrate the effectiveness of our approach in a MRI segmentation problem. In order to dramatically reduce the computation time to find near-optimal cluster centers, we use an alternate representation of the search space. Our experiments indicate encouraging results in producing stable clustering for the given problem as compared to using an FCM with randomly initialized cluster centers.</abstract><pub>IEEE</pub><doi>10.1109/TENCON.2009.5396049</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
| recordid | cdi_ieee_primary_5396049 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biomedical imaging Clustering algorithms Digital images Evolutionary computation Image analysis Image segmentation Magnetic resonance imaging Radiology Search methods Space exploration |
| title | Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images |
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