Brain Image Segmentation Based on FCM Clustering Algorithm and Rough Set
In this paper, a new image segmentation method is proposed by combining the FCM clustering algorithm with a rough set theory. First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regio...
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| Veröffentlicht in: | IEEE access 2019, Vol.7, p.12386-12396 |
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| creator | Huang, Hong Meng, Fanzhi Zhou, Shaohua Jiang, Feng Manogaran, Gunasekaran |
| description | In this paper, a new image segmentation method is proposed by combining the FCM clustering algorithm with a rough set theory. First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regions based on the indistinguishable relationship of attributes. Then, the weight values of each attribute are obtained by value reduction and used as the basis to calculate the difference between regions and then the similarity evaluation of each region is realized through the equivalence relationship defined by the difference degree. Finally, the final equivalence relation defined by similarity is used to merge regions and complete image segmentation. This method is validated in the segmentation of artificially generated images, brain CT images, and MRI images. The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region. And, the experimental results also prove that the algorithm has strong anti-noise ability. |
| doi_str_mv | 10.1109/ACCESS.2019.2893063 |
| format | Article |
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First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regions based on the indistinguishable relationship of attributes. Then, the weight values of each attribute are obtained by value reduction and used as the basis to calculate the difference between regions and then the similarity evaluation of each region is realized through the equivalence relationship defined by the difference degree. Finally, the final equivalence relation defined by similarity is used to merge regions and complete image segmentation. This method is validated in the segmentation of artificially generated images, brain CT images, and MRI images. The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region. 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(IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c458t-778fdad897723b6acb7347ae70151fd5bb2cab25bb33d54732c1fe326de481da3</citedby><cites>FETCH-LOGICAL-c458t-778fdad897723b6acb7347ae70151fd5bb2cab25bb33d54732c1fe326de481da3</cites><orcidid>0000-0002-0973-7231 ; 0000-0001-8342-1211 ; 0000-0003-4083-6163</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8612906$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Huang, Hong</creatorcontrib><creatorcontrib>Meng, Fanzhi</creatorcontrib><creatorcontrib>Zhou, Shaohua</creatorcontrib><creatorcontrib>Jiang, Feng</creatorcontrib><creatorcontrib>Manogaran, Gunasekaran</creatorcontrib><title>Brain Image Segmentation Based on FCM Clustering Algorithm and Rough Set</title><title>IEEE access</title><addtitle>Access</addtitle><description>In this paper, a new image segmentation method is proposed by combining the FCM clustering algorithm with a rough set theory. First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regions based on the indistinguishable relationship of attributes. Then, the weight values of each attribute are obtained by value reduction and used as the basis to calculate the difference between regions and then the similarity evaluation of each region is realized through the equivalence relationship defined by the difference degree. Finally, the final equivalence relation defined by similarity is used to merge regions and complete image segmentation. This method is validated in the segmentation of artificially generated images, brain CT images, and MRI images. The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region. And, the experimental results also prove that the algorithm has strong anti-noise ability.</description><subject>Algorithms</subject><subject>Brain</subject><subject>Brain image segmentation</subject><subject>Classification algorithms</subject><subject>Clustering</subject><subject>Clustering algorithms</subject><subject>Clustering methods</subject><subject>Computed tomography</subject><subject>Equivalence</subject><subject>Error reduction</subject><subject>FCM clustering</subject><subject>Image segmentation</subject><subject>Kernel</subject><subject>Magnetic resonance imaging</subject><subject>Medical imaging</subject><subject>rough set</subject><subject>Rough sets</subject><subject>Set theory</subject><subject>Similarity</subject><subject>system</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1rwzAMDWODla6_oJfAzun8GTvHNrRroWOwbmfj2E6aksSd4xz27-cupUwXiSe9J6EXRXMIFhCC7GWZ5-vDYYEAzBaIZxik-C6aIJhmCaY4vf9XP0azvj-BEDxAlE2i7crJuot3raxMfDBVazovfW27eCV7o-NQbPK3OG-G3htXd1W8bCrran9sY9np-MMO1TEQ_VP0UMqmN7NrnkZfm_Vnvk3276-7fLlPFKHcJ4zxUkvNM8YQLlKpCoYJk4YBSGGpaVEgJQsUMsaaEoaRgqXBKNWGcKglnka7UVdbeRJnV7fS_Qgra_EHWFcJ6XytGiM4TRkvGJdloQkM7zFIa6UxQaxQAJGg9TxqnZ39HkzvxckOrgvnC0QoTQFhAIcpPE4pZ_vemfK2FQJxcUCMDoiLA-LqQGDNR1ZtjLkxeApRFtq_e2SAPA</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Huang, Hong</creator><creator>Meng, Fanzhi</creator><creator>Zhou, Shaohua</creator><creator>Jiang, Feng</creator><creator>Manogaran, Gunasekaran</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Algorithms Brain Brain image segmentation Classification algorithms Clustering Clustering algorithms Clustering methods Computed tomography Equivalence Error reduction FCM clustering Image segmentation Kernel Magnetic resonance imaging Medical imaging rough set Rough sets Set theory Similarity system |
| title | Brain Image Segmentation Based on FCM Clustering Algorithm and Rough Set |
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