Data-driven homologue matching for chromosome identification
Karyotyping involves the visualization and classification of chromosomes into standard classes. In "normal" human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human c...
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| Veröffentlicht in: | IEEE transactions on medical imaging 1998-06, Vol.17 (3), p.451-462 |
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| container_title | IEEE transactions on medical imaging |
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| creator | Stanley, R.J. Keller, J.M. Gader, P. Caldwell, C.W. |
| description | Karyotyping involves the visualization and classification of chromosomes into standard classes. In "normal" human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human chromosome image analysis presuppose cell normalcy, containing 46 chromosomes within a metaphase spread with two chromosomes per class. This is an acceptable assumption for routine automated chromosome image analysis. However, many genetic abnormalities are directly linked to structural or numerical aberrations of chromosomes within the metaphase spread. Thus, two chromosomes per class cannot be assumed for anomaly analysis. This paper presents the development of image analysis techniques which are extendible to detecting numerical aberrations evolving from structural abnormalities. Specifically, an approach to identifying "normal" chromosomes from selected class(es) within a metaphase spread is presented. Chromosome assignment to a specific class is initially based on neural networks, followed by banding pattern and centromeric index criteria checking, and concluding with homologue matching. Experimental results are presented comparing neural networks as the sole classifier to the authors' homologue matcher for identifying class 17 within normal and abnormal metaphase spreads. |
| doi_str_mv | 10.1109/42.712134 |
| format | Article |
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Chromosome assignment to a specific class is initially based on neural networks, followed by banding pattern and centromeric index criteria checking, and concluding with homologue matching. Experimental results are presented comparing neural networks as the sole classifier to the authors' homologue matcher for identifying class 17 within normal and abnormal metaphase spreads.</description><identifier>ISSN: 0278-0062</identifier><identifier>EISSN: 1558-254X</identifier><identifier>DOI: 10.1109/42.712134</identifier><identifier>PMID: 9735908</identifier><identifier>CODEN: ITMID4</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Biological and medical sciences ; Biological cells ; Cells (biology) ; Chromatin. Chromosome ; Chromosome Banding ; Chromosomes ; Computer science ; Dynamic programming ; Fundamental and applied biological sciences. 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In "normal" human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human chromosome image analysis presuppose cell normalcy, containing 46 chromosomes within a metaphase spread with two chromosomes per class. This is an acceptable assumption for routine automated chromosome image analysis. However, many genetic abnormalities are directly linked to structural or numerical aberrations of chromosomes within the metaphase spread. Thus, two chromosomes per class cannot be assumed for anomaly analysis. This paper presents the development of image analysis techniques which are extendible to detecting numerical aberrations evolving from structural abnormalities. Specifically, an approach to identifying "normal" chromosomes from selected class(es) within a metaphase spread is presented. Chromosome assignment to a specific class is initially based on neural networks, followed by banding pattern and centromeric index criteria checking, and concluding with homologue matching. Experimental results are presented comparing neural networks as the sole classifier to the authors' homologue matcher for identifying class 17 within normal and abnormal metaphase spreads.</description><subject>Biological and medical sciences</subject><subject>Biological cells</subject><subject>Cells (biology)</subject><subject>Chromatin. Chromosome</subject><subject>Chromosome Banding</subject><subject>Chromosomes</subject><subject>Computer science</subject><subject>Dynamic programming</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Genetics</subject><subject>Humans</subject><subject>Image analysis</subject><subject>Image Interpretation, Computer-Assisted</subject><subject>Karyotyping - methods</subject><subject>Metaphase</subject><subject>Molecular and cellular biology</subject><subject>Molecular genetics</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Pattern matching</subject><subject>Pattern recognition</subject><subject>Visualization</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0M9LwzAUB_AgypzTg1dB6EEED50vv9oGvMj8CQMvCt5Klr5skbbRpBP87-1Y2XWnHL4f3nv5EnJOYUopqFvBpjlllIsDMqZSFimT4vOQjIHlRQqQsWNyEuMXABUS1IiMVM6lgmJM7h50p9MquF9sk5VvfO2Xa0wa3ZmVa5eJ9SExq9AH0TeYuArbzllndOd8e0qOrK4jng3vhHw8Pb7PXtL52_Pr7H6eGq6gSxcImtvMZoUqdI7CAFQWpRCVFFYW3GrOQUmT8SwHgyBt1d-PVa-yDBD5hFxv534H_7PG2JWNiwbrWrfo17HM-zW0_85eyAqWq0Jk-yHliqt8A2-20AQfY0BbfgfX6PBXUig33ZeCldvue3s5DF0vGqx2cii7z6-GXEejaxt0a1zcMcYVZVL17GLLHCLu0mHHPzb5ks4</recordid><startdate>19980601</startdate><enddate>19980601</enddate><creator>Stanley, R.J.</creator><creator>Keller, J.M.</creator><creator>Gader, P.</creator><creator>Caldwell, C.W.</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></search><sort><creationdate>19980601</creationdate><title>Data-driven homologue matching for chromosome identification</title><author>Stanley, R.J. ; Keller, J.M. ; Gader, P. ; Caldwell, C.W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c390t-be0a3f6f6898a7e4c00dfe544d54f583fa33095c63670ce05fd254ed0df660ee3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Biological and medical sciences</topic><topic>Biological cells</topic><topic>Cells (biology)</topic><topic>Chromatin. Chromosome</topic><topic>Chromosome Banding</topic><topic>Chromosomes</topic><topic>Computer science</topic><topic>Dynamic programming</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Genetics</topic><topic>Humans</topic><topic>Image analysis</topic><topic>Image Interpretation, Computer-Assisted</topic><topic>Karyotyping - methods</topic><topic>Metaphase</topic><topic>Molecular and cellular biology</topic><topic>Molecular genetics</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Pattern matching</topic><topic>Pattern recognition</topic><topic>Visualization</topic><toplevel>online_resources</toplevel><creatorcontrib>Stanley, R.J.</creatorcontrib><creatorcontrib>Keller, J.M.</creatorcontrib><creatorcontrib>Gader, P.</creatorcontrib><creatorcontrib>Caldwell, C.W.</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><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Stanley, R.J.</au><au>Keller, J.M.</au><au>Gader, P.</au><au>Caldwell, C.W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data-driven homologue matching for chromosome identification</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>1998-06-01</date><risdate>1998</risdate><volume>17</volume><issue>3</issue><spage>451</spage><epage>462</epage><pages>451-462</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><abstract>Karyotyping involves the visualization and classification of chromosomes into standard classes. In "normal" human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human chromosome image analysis presuppose cell normalcy, containing 46 chromosomes within a metaphase spread with two chromosomes per class. This is an acceptable assumption for routine automated chromosome image analysis. However, many genetic abnormalities are directly linked to structural or numerical aberrations of chromosomes within the metaphase spread. Thus, two chromosomes per class cannot be assumed for anomaly analysis. This paper presents the development of image analysis techniques which are extendible to detecting numerical aberrations evolving from structural abnormalities. Specifically, an approach to identifying "normal" chromosomes from selected class(es) within a metaphase spread is presented. Chromosome assignment to a specific class is initially based on neural networks, followed by banding pattern and centromeric index criteria checking, and concluding with homologue matching. Experimental results are presented comparing neural networks as the sole classifier to the authors' homologue matcher for identifying class 17 within normal and abnormal metaphase spreads.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>9735908</pmid><doi>10.1109/42.712134</doi><tpages>12</tpages></addata></record> |
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| subjects | Biological and medical sciences Biological cells Cells (biology) Chromatin. Chromosome Chromosome Banding Chromosomes Computer science Dynamic programming Fundamental and applied biological sciences. Psychology Genetics Humans Image analysis Image Interpretation, Computer-Assisted Karyotyping - methods Metaphase Molecular and cellular biology Molecular genetics Neural networks Neural Networks (Computer) Pattern matching Pattern recognition Visualization |
| title | Data-driven homologue matching for chromosome identification |
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