Adaptive fuzzy segmentation of magnetic resonance images
An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-mea...
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| Veröffentlicht in: | IEEE transactions on medical imaging 1999-09, Vol.18 (9), p.737-752 |
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| creator | Pham, D.L. Prince, J.L. |
| description | An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, the authors fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, they also describe a new faster multigrid-based algorithm for its implementation. They show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images. |
| doi_str_mv | 10.1109/42.802752 |
| format | Article |
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The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, the authors fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, they also describe a new faster multigrid-based algorithm for its implementation. They show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. 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The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, the authors fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, they also describe a new faster multigrid-based algorithm for its implementation. They show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.</description><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Biological and medical sciences</subject><subject>Brain - anatomy & histology</subject><subject>Computer Simulation</subject><subject>Filtering</subject><subject>Fuzzy Logic</subject><subject>Fuzzy sets</subject><subject>Humans</subject><subject>Image analysis</subject><subject>Image Processing, Computer-Assisted</subject><subject>Image segmentation</subject><subject>Investigative techniques, diagnostic techniques (general aspects)</subject><subject>Iterative algorithms</subject><subject>Magnetic noise</subject><subject>Magnetic resonance</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Medical sciences</subject><subject>Miscellaneous. Technology</subject><subject>Nonuniform electric fields</subject><subject>Radiodiagnosis. Nmr imagery. Nmr spectrometry</subject><subject>Surface fitting</subject><subject>Two dimensional displays</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0c9LwzAUB_AgipvTg1cP0oMoHjrz8qvJcQx_wcCLgreSpi-jsrWz6YTtrzfSoZ70FHj58B58v4ScAh0DUHMj2FhTlkm2R4YgpU6ZFK_7ZBhnOqVUsQE5CuGNUhCSmkMyACoz4JkZEj0p7aqrPjDx6-12kwScL7HubFc1ddL4ZGnnNXaVS1oMTW1rh0kVZxiOyYG3i4Anu3dEXu5un6cP6ezp_nE6maVOMN6l3JRl4QqrtLJGSO-1hpJJynmRcUBmJRO6tEpi4TMHmme29M54Y8CD9YyPyFW_d9U272sMXb6sgsPFwtbYrENugCpBM8WjvPxTKsMMKAP_QqaZAiXV_zBmKBRkEV730LVNCC36fNXGmNpNDjT_qigXLO8rivZ8t3RdLLH8JftOIrjYARucXfg2pl6FH8fAaEMjO-tZhYjfv7sjn9nPnzY</recordid><startdate>19990901</startdate><enddate>19990901</enddate><creator>Pham, D.L.</creator><creator>Prince, J.L.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7U5</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><scope>7QO</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>19990901</creationdate><title>Adaptive fuzzy segmentation of magnetic resonance images</title><author>Pham, D.L. ; Prince, J.L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c423t-39ddbcba686a945ff881d25033b731e2a5248da65ebf7c1837adfc9f991f1af23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Adaptive algorithms</topic><topic>Algorithms</topic><topic>Biological and medical sciences</topic><topic>Brain - anatomy & histology</topic><topic>Computer Simulation</topic><topic>Filtering</topic><topic>Fuzzy Logic</topic><topic>Fuzzy sets</topic><topic>Humans</topic><topic>Image analysis</topic><topic>Image Processing, Computer-Assisted</topic><topic>Image segmentation</topic><topic>Investigative techniques, diagnostic techniques (general aspects)</topic><topic>Iterative algorithms</topic><topic>Magnetic noise</topic><topic>Magnetic resonance</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Medical sciences</topic><topic>Miscellaneous. Technology</topic><topic>Nonuniform electric fields</topic><topic>Radiodiagnosis. Nmr imagery. Nmr spectrometry</topic><topic>Surface fitting</topic><topic>Two dimensional displays</topic><toplevel>online_resources</toplevel><creatorcontrib>Pham, D.L.</creatorcontrib><creatorcontrib>Prince, J.L.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Solid State and Superconductivity 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><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pham, D.L.</au><au>Prince, J.L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive fuzzy segmentation of magnetic resonance images</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>1999-09-01</date><risdate>1999</risdate><volume>18</volume><issue>9</issue><spage>737</spage><epage>752</epage><pages>737-752</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><abstract>An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, the authors fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, they also describe a new faster multigrid-based algorithm for its implementation. They show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. 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| subjects | Adaptive algorithms Algorithms Biological and medical sciences Brain - anatomy & histology Computer Simulation Filtering Fuzzy Logic Fuzzy sets Humans Image analysis Image Processing, Computer-Assisted Image segmentation Investigative techniques, diagnostic techniques (general aspects) Iterative algorithms Magnetic noise Magnetic resonance Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical sciences Miscellaneous. Technology Nonuniform electric fields Radiodiagnosis. Nmr imagery. Nmr spectrometry Surface fitting Two dimensional displays |
| title | Adaptive fuzzy segmentation of magnetic resonance images |
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