An evolutionary Michigan recurrent fuzzy system for nuclei classification in cytological images using nuclear chromatin distribution
The objective of this research is to carry out the classification of cellular nuclei in cytological pleural fluid images. The article focuses on the feature extraction and classification processes. The extracted feature is a spatial measurement of the chromatin distribution in cellular nuclei. The d...
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| Veröffentlicht in: | Journal of biomedical informatics 2006-12, Vol.39 (6), p.573-588 |
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| container_title | Journal of biomedical informatics |
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| creator | Alayón, S. Estévez, J.I. Sigut, J. Sánchez, J.L. Toledo, P. |
| description | The objective of this research is to carry out the classification of cellular nuclei in cytological pleural fluid images. The article focuses on the feature extraction and classification processes. The extracted feature is a spatial measurement of the chromatin distribution in cellular nuclei. The designed classifiers are fuzzy classifiers that carry out supervised classification. The classifier system’s inputs are data series that represent these texture measurements.
The classifier is built on a
Recurrent Fuzzy System (RFS). An evolutionary algorithm inspired by the Michigan approach is used to find an optimal RFS to classify different patterns expressed as data series.
The effectiveness of the proposed classifier system is compared with other existing classification methods and evaluated via
Receiver Operating Characteristic (ROC) analysis. We have obtained RFS based classifiers that perform with sensitivity values between 82.26 and 93.55% and with specificity values between 80.65 and 90.32%. The behavior of the proposed chromatin measurement is also compared with other texture measurements.
The RFS based classifiers were successfully applied to the proposed data series that represent the chromatin distribution in cellular nuclei. These fuzzy classifiers present the highest classification efficiency and the ROC analysis confirms their suitable behavior. |
| doi_str_mv | 10.1016/j.jbi.2006.03.001 |
| format | Article |
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The classifier is built on a
Recurrent Fuzzy System (RFS). An evolutionary algorithm inspired by the Michigan approach is used to find an optimal RFS to classify different patterns expressed as data series.
The effectiveness of the proposed classifier system is compared with other existing classification methods and evaluated via
Receiver Operating Characteristic (ROC) analysis. We have obtained RFS based classifiers that perform with sensitivity values between 82.26 and 93.55% and with specificity values between 80.65 and 90.32%. The behavior of the proposed chromatin measurement is also compared with other texture measurements.
The RFS based classifiers were successfully applied to the proposed data series that represent the chromatin distribution in cellular nuclei. These fuzzy classifiers present the highest classification efficiency and the ROC analysis confirms their suitable behavior.</description><identifier>ISSN: 1532-0464</identifier><identifier>EISSN: 1532-0480</identifier><identifier>DOI: 10.1016/j.jbi.2006.03.001</identifier><identifier>PMID: 16624624</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Algorithms ; Carcinoma - pathology ; Cell Biology - standards ; Cell Nucleus - metabolism ; Cell Nucleus - pathology ; Chromatin - chemistry ; Computational Biology - methods ; Cytological images ; Epithelium - pathology ; Evolution, Molecular ; Fuzzy Logic ; Genetic algorithm ; Humans ; Markov Chains ; Models, Theoretical ; Nuclei chromatin distribution ; Nuclei classification ; Pattern recognition ; Probability ; Recurrent fuzzy system ; ROC analysis ; ROC Curve ; Sensitivity and Specificity</subject><ispartof>Journal of biomedical informatics, 2006-12, Vol.39 (6), p.573-588</ispartof><rights>2006 Elsevier Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c425t-d9b075ed59b41ea355d35968de4c5f264c23bfb814db37394c2aad8283a7e3333</citedby><cites>FETCH-LOGICAL-c425t-d9b075ed59b41ea355d35968de4c5f264c23bfb814db37394c2aad8283a7e3333</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jbi.2006.03.001$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16624624$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Alayón, S.</creatorcontrib><creatorcontrib>Estévez, J.I.</creatorcontrib><creatorcontrib>Sigut, J.</creatorcontrib><creatorcontrib>Sánchez, J.L.</creatorcontrib><creatorcontrib>Toledo, P.</creatorcontrib><title>An evolutionary Michigan recurrent fuzzy system for nuclei classification in cytological images using nuclear chromatin distribution</title><title>Journal of biomedical informatics</title><addtitle>J Biomed Inform</addtitle><description>The objective of this research is to carry out the classification of cellular nuclei in cytological pleural fluid images. The article focuses on the feature extraction and classification processes. The extracted feature is a spatial measurement of the chromatin distribution in cellular nuclei. The designed classifiers are fuzzy classifiers that carry out supervised classification. The classifier system’s inputs are data series that represent these texture measurements.
The classifier is built on a
Recurrent Fuzzy System (RFS). An evolutionary algorithm inspired by the Michigan approach is used to find an optimal RFS to classify different patterns expressed as data series.
The effectiveness of the proposed classifier system is compared with other existing classification methods and evaluated via
Receiver Operating Characteristic (ROC) analysis. We have obtained RFS based classifiers that perform with sensitivity values between 82.26 and 93.55% and with specificity values between 80.65 and 90.32%. The behavior of the proposed chromatin measurement is also compared with other texture measurements.
The RFS based classifiers were successfully applied to the proposed data series that represent the chromatin distribution in cellular nuclei. These fuzzy classifiers present the highest classification efficiency and the ROC analysis confirms their suitable behavior.</description><subject>Algorithms</subject><subject>Carcinoma - pathology</subject><subject>Cell Biology - standards</subject><subject>Cell Nucleus - metabolism</subject><subject>Cell Nucleus - pathology</subject><subject>Chromatin - chemistry</subject><subject>Computational Biology - methods</subject><subject>Cytological images</subject><subject>Epithelium - pathology</subject><subject>Evolution, Molecular</subject><subject>Fuzzy Logic</subject><subject>Genetic algorithm</subject><subject>Humans</subject><subject>Markov Chains</subject><subject>Models, Theoretical</subject><subject>Nuclei chromatin distribution</subject><subject>Nuclei classification</subject><subject>Pattern recognition</subject><subject>Probability</subject><subject>Recurrent fuzzy system</subject><subject>ROC analysis</subject><subject>ROC Curve</subject><subject>Sensitivity and Specificity</subject><issn>1532-0464</issn><issn>1532-0480</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkUtv1DAURi1ERR_wA9ggr9hNsONHErGqqkKRirpp15Zj30zvKLGLnVRK1_xwPMwIdmBZ8rV1vmvZh5D3nFWccf1pV-16rGrGdMVExRh_Rc64EvWGyZa9_lNreUrOc94VgCul35BTrnUtyzwjPy8Dhec4LjPGYNNKv6N7xK0NNIFbUoIw02F5eVlpXvMMEx1iomFxIyB1o80ZB3R2H6YYqFvnOMZtORkpTnYLmS4Zw_aQsIm6xxSnggfqMc8J-9_3viUngx0zvDuuF-Thy_X91c3m9u7rt6vL242TtZo3vutZo8CrrpccrFDKC9Xp1oN0aqi1dLXoh77l0veiEV3ZW-vbuhW2AVHGBfl46PuU4o8F8mwmzA7G0QaISza65aJRkv0X5J1s6oapAvID6FLMOcFgnlJ5eFoNZ2bvyOxMcWT2jgwTpigomQ_H5ks_gf-bOEopwOcDAOUvnhGSyQ4hOPBYpMzGR_xH-18aDaVd</recordid><startdate>20061201</startdate><enddate>20061201</enddate><creator>Alayón, S.</creator><creator>Estévez, J.I.</creator><creator>Sigut, J.</creator><creator>Sánchez, J.L.</creator><creator>Toledo, P.</creator><general>Elsevier Inc</general><scope>6I.</scope><scope>AAFTH</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>7QO</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20061201</creationdate><title>An evolutionary Michigan recurrent fuzzy system for nuclei classification in cytological images using nuclear chromatin distribution</title><author>Alayón, S. ; Estévez, J.I. ; Sigut, J. ; Sánchez, J.L. ; Toledo, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c425t-d9b075ed59b41ea355d35968de4c5f264c23bfb814db37394c2aad8283a7e3333</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Carcinoma - pathology</topic><topic>Cell Biology - standards</topic><topic>Cell Nucleus - metabolism</topic><topic>Cell Nucleus - pathology</topic><topic>Chromatin - chemistry</topic><topic>Computational Biology - methods</topic><topic>Cytological images</topic><topic>Epithelium - pathology</topic><topic>Evolution, Molecular</topic><topic>Fuzzy Logic</topic><topic>Genetic algorithm</topic><topic>Humans</topic><topic>Markov Chains</topic><topic>Models, Theoretical</topic><topic>Nuclei chromatin distribution</topic><topic>Nuclei classification</topic><topic>Pattern recognition</topic><topic>Probability</topic><topic>Recurrent fuzzy system</topic><topic>ROC analysis</topic><topic>ROC Curve</topic><topic>Sensitivity and Specificity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alayón, S.</creatorcontrib><creatorcontrib>Estévez, J.I.</creatorcontrib><creatorcontrib>Sigut, J.</creatorcontrib><creatorcontrib>Sánchez, J.L.</creatorcontrib><creatorcontrib>Toledo, P.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of biomedical informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alayón, S.</au><au>Estévez, J.I.</au><au>Sigut, J.</au><au>Sánchez, J.L.</au><au>Toledo, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An evolutionary Michigan recurrent fuzzy system for nuclei classification in cytological images using nuclear chromatin distribution</atitle><jtitle>Journal of biomedical informatics</jtitle><addtitle>J Biomed Inform</addtitle><date>2006-12-01</date><risdate>2006</risdate><volume>39</volume><issue>6</issue><spage>573</spage><epage>588</epage><pages>573-588</pages><issn>1532-0464</issn><eissn>1532-0480</eissn><abstract>The objective of this research is to carry out the classification of cellular nuclei in cytological pleural fluid images. The article focuses on the feature extraction and classification processes. The extracted feature is a spatial measurement of the chromatin distribution in cellular nuclei. The designed classifiers are fuzzy classifiers that carry out supervised classification. The classifier system’s inputs are data series that represent these texture measurements.
The classifier is built on a
Recurrent Fuzzy System (RFS). An evolutionary algorithm inspired by the Michigan approach is used to find an optimal RFS to classify different patterns expressed as data series.
The effectiveness of the proposed classifier system is compared with other existing classification methods and evaluated via
Receiver Operating Characteristic (ROC) analysis. We have obtained RFS based classifiers that perform with sensitivity values between 82.26 and 93.55% and with specificity values between 80.65 and 90.32%. The behavior of the proposed chromatin measurement is also compared with other texture measurements.
The RFS based classifiers were successfully applied to the proposed data series that represent the chromatin distribution in cellular nuclei. These fuzzy classifiers present the highest classification efficiency and the ROC analysis confirms their suitable behavior.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>16624624</pmid><doi>10.1016/j.jbi.2006.03.001</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Carcinoma - pathology Cell Biology - standards Cell Nucleus - metabolism Cell Nucleus - pathology Chromatin - chemistry Computational Biology - methods Cytological images Epithelium - pathology Evolution, Molecular Fuzzy Logic Genetic algorithm Humans Markov Chains Models, Theoretical Nuclei chromatin distribution Nuclei classification Pattern recognition Probability Recurrent fuzzy system ROC analysis ROC Curve Sensitivity and Specificity |
| title | An evolutionary Michigan recurrent fuzzy system for nuclei classification in cytological images using nuclear chromatin distribution |
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