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X-ray image classification using Random Forests with Local Binary Patterns
This paper presents a novel algorithm for the efficient classification of X-ray images to enhance the accuracy and performance. As for describing the characteristics of X-ray image, new Local Binary Patterns (LBP) is employed that allows simple and efficient feature extraction for texture informatio...
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| Language: | English |
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| container_end_page | 3194 |
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| container_start_page | 3190 |
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| container_volume | 6 |
| creator | Seong-Hoon Kim Ji-Hyun Lee Byoungchul Ko Jae-Yeal Nam |
| description | This paper presents a novel algorithm for the efficient classification of X-ray images to enhance the accuracy and performance. As for describing the characteristics of X-ray image, new Local Binary Patterns (LBP) is employed that allows simple and efficient feature extraction for texture information. To achieve fast and accurate classification task, Random Forests that is decision tree based ensemble classifier is applied. Comparing with other feature descriptors and classifiers, the testing results show that the proposed method improves accuracy, especially the speed for either training or testing. |
| doi_str_mv | 10.1109/ICMLC.2010.5580711 |
| format | conference_proceeding |
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Comparing with other feature descriptors and classifiers, the testing results show that the proposed method improves accuracy, especially the speed for either training or testing.</description><subject>Biomedical imaging</subject><subject>Classification algorithms</subject><subject>Feature extraction</subject><subject>Histograms</subject><subject>Image classification</subject><subject>Local Binary Patterns</subject><subject>Random Forests</subject><subject>Training</subject><subject>X-ray image classification</subject><subject>X-ray imaging</subject><issn>2160-133X</issn><isbn>9781424465262</isbn><isbn>1424465265</isbn><isbn>9781424465255</isbn><isbn>1424465273</isbn><isbn>1424465257</isbn><isbn>9781424465279</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpVkMFOwzAQRI0AiarkB-DiH0jx2l47OUJES1EQCPXQW7VJnWKUJsgOQvl7ItELcxnNHEZPw9gNiAWAyO_WxUtZLKSYMmImLMAZS3KbgZZaG5SI5_-ykRdsJsGIFJTaXrEkxk8xSaOEHGfseZsGGrk_0sHxuqUYfeNrGnzf8e_ouwN_p27fH_myDy4Okf_44YOXfU0tf_AdhZG_0TC40MVrdtlQG11y8jnbLB83xVNavq7WxX2Z-lwMKQpSYFHWBEY3VebM3sBUqAYmyioDNGQb0jbTAvNMm9xKZ7RCVykh0Ko5u_2b9c653VeY0MO4O52hfgGqA07E</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Seong-Hoon Kim</creator><creator>Ji-Hyun Lee</creator><creator>Byoungchul Ko</creator><creator>Jae-Yeal Nam</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201007</creationdate><title>X-ray image classification using Random Forests with Local Binary Patterns</title><author>Seong-Hoon Kim ; Ji-Hyun Lee ; Byoungchul Ko ; Jae-Yeal Nam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-50a31752ca164fb8e6d611753f1160b8156a7fa4784059846972e6435eb300573</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Biomedical imaging</topic><topic>Classification algorithms</topic><topic>Feature extraction</topic><topic>Histograms</topic><topic>Image classification</topic><topic>Local Binary Patterns</topic><topic>Random Forests</topic><topic>Training</topic><topic>X-ray image classification</topic><topic>X-ray imaging</topic><toplevel>online_resources</toplevel><creatorcontrib>Seong-Hoon Kim</creatorcontrib><creatorcontrib>Ji-Hyun Lee</creatorcontrib><creatorcontrib>Byoungchul Ko</creatorcontrib><creatorcontrib>Jae-Yeal Nam</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Seong-Hoon Kim</au><au>Ji-Hyun Lee</au><au>Byoungchul Ko</au><au>Jae-Yeal Nam</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>X-ray image classification using Random Forests with Local Binary Patterns</atitle><btitle>2010 International Conference on Machine Learning and Cybernetics</btitle><stitle>ICMLC</stitle><date>2010-07</date><risdate>2010</risdate><volume>6</volume><spage>3190</spage><epage>3194</epage><pages>3190-3194</pages><issn>2160-133X</issn><isbn>9781424465262</isbn><isbn>1424465265</isbn><eisbn>9781424465255</eisbn><eisbn>1424465273</eisbn><eisbn>1424465257</eisbn><eisbn>9781424465279</eisbn><abstract>This paper presents a novel algorithm for the efficient classification of X-ray images to enhance the accuracy and performance. As for describing the characteristics of X-ray image, new Local Binary Patterns (LBP) is employed that allows simple and efficient feature extraction for texture information. To achieve fast and accurate classification task, Random Forests that is decision tree based ensemble classifier is applied. Comparing with other feature descriptors and classifiers, the testing results show that the proposed method improves accuracy, especially the speed for either training or testing.</abstract><pub>IEEE</pub><doi>10.1109/ICMLC.2010.5580711</doi><tpages>5</tpages></addata></record> |
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| identifier | ISSN: 2160-133X |
| ispartof | 2010 International Conference on Machine Learning and Cybernetics, 2010, Vol.6, p.3190-3194 |
| issn | 2160-133X |
| language | eng |
| recordid | cdi_ieee_primary_5580711 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biomedical imaging Classification algorithms Feature extraction Histograms Image classification Local Binary Patterns Random Forests Training X-ray image classification X-ray imaging |
| title | X-ray image classification using Random Forests with Local Binary Patterns |
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