A Content-Based Approach to Medical Images Retrieval
Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tarjoman, Mana [verfasserIn] Fatemizadeh, Emad [verfasserIn] Badie, Kambiz [verfasserIn] |
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Englisch |
| Erschienen: |
2013 |
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Enthalten in: International journal of healthcare information systems and informatics - Hershey, Pa : IGI Global, 2006, 8(2013), 2, Seite 15-27 |
|---|---|
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volume:8 ; year:2013 ; number:2 ; pages:15-27 |
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| DOI / URN: |
10.4018/jhisi.2013040102 |
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| 520 | |a Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency | ||
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10.4018/jhisi.2013040102 doi (DE-627)NLEJ251807991 (VZGNL)10.4018/jhisi.2013040102 DE-627 ger DE-627 rakwb eng Tarjoman, Mana verfasserin aut A Content-Based Approach to Medical Images Retrieval 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency Adaptive Neuro-Fuzzy Inference System (ANFIS) Content-Based Image Retrieval (CBIR) Feature Extraction Magnetic Resonance Image Medical Image Databases Fatemizadeh, Emad verfasserin aut Badie, Kambiz verfasserin aut Enthalten in International journal of healthcare information systems and informatics Hershey, Pa : IGI Global, 2006 8(2013), 2, Seite 15-27 Online-Ressource (DE-627)NLEJ244419086 (DE-600)2242817-3 1555-340X nnns volume:8 year:2013 number:2 pages:15-27 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 8 2013 2 15-27 |
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10.4018/jhisi.2013040102 doi (DE-627)NLEJ251807991 (VZGNL)10.4018/jhisi.2013040102 DE-627 ger DE-627 rakwb eng Tarjoman, Mana verfasserin aut A Content-Based Approach to Medical Images Retrieval 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency Adaptive Neuro-Fuzzy Inference System (ANFIS) Content-Based Image Retrieval (CBIR) Feature Extraction Magnetic Resonance Image Medical Image Databases Fatemizadeh, Emad verfasserin aut Badie, Kambiz verfasserin aut Enthalten in International journal of healthcare information systems and informatics Hershey, Pa : IGI Global, 2006 8(2013), 2, Seite 15-27 Online-Ressource (DE-627)NLEJ244419086 (DE-600)2242817-3 1555-340X nnns volume:8 year:2013 number:2 pages:15-27 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 8 2013 2 15-27 |
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10.4018/jhisi.2013040102 doi (DE-627)NLEJ251807991 (VZGNL)10.4018/jhisi.2013040102 DE-627 ger DE-627 rakwb eng Tarjoman, Mana verfasserin aut A Content-Based Approach to Medical Images Retrieval 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency Adaptive Neuro-Fuzzy Inference System (ANFIS) Content-Based Image Retrieval (CBIR) Feature Extraction Magnetic Resonance Image Medical Image Databases Fatemizadeh, Emad verfasserin aut Badie, Kambiz verfasserin aut Enthalten in International journal of healthcare information systems and informatics Hershey, Pa : IGI Global, 2006 8(2013), 2, Seite 15-27 Online-Ressource (DE-627)NLEJ244419086 (DE-600)2242817-3 1555-340X nnns volume:8 year:2013 number:2 pages:15-27 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 8 2013 2 15-27 |
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10.4018/jhisi.2013040102 doi (DE-627)NLEJ251807991 (VZGNL)10.4018/jhisi.2013040102 DE-627 ger DE-627 rakwb eng Tarjoman, Mana verfasserin aut A Content-Based Approach to Medical Images Retrieval 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency Adaptive Neuro-Fuzzy Inference System (ANFIS) Content-Based Image Retrieval (CBIR) Feature Extraction Magnetic Resonance Image Medical Image Databases Fatemizadeh, Emad verfasserin aut Badie, Kambiz verfasserin aut Enthalten in International journal of healthcare information systems and informatics Hershey, Pa : IGI Global, 2006 8(2013), 2, Seite 15-27 Online-Ressource (DE-627)NLEJ244419086 (DE-600)2242817-3 1555-340X nnns volume:8 year:2013 number:2 pages:15-27 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 8 2013 2 15-27 |
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10.4018/jhisi.2013040102 doi (DE-627)NLEJ251807991 (VZGNL)10.4018/jhisi.2013040102 DE-627 ger DE-627 rakwb eng Tarjoman, Mana verfasserin aut A Content-Based Approach to Medical Images Retrieval 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency Adaptive Neuro-Fuzzy Inference System (ANFIS) Content-Based Image Retrieval (CBIR) Feature Extraction Magnetic Resonance Image Medical Image Databases Fatemizadeh, Emad verfasserin aut Badie, Kambiz verfasserin aut Enthalten in International journal of healthcare information systems and informatics Hershey, Pa : IGI Global, 2006 8(2013), 2, Seite 15-27 Online-Ressource (DE-627)NLEJ244419086 (DE-600)2242817-3 1555-340X nnns volume:8 year:2013 number:2 pages:15-27 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 8 2013 2 15-27 |
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Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency |
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Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ251807991</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205143917.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231128s2013 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/jhisi.2013040102</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ251807991</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/jhisi.2013040102</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tarjoman, Mana</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Content-Based Approach to Medical Images Retrieval</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Adaptive Neuro-Fuzzy Inference System (ANFIS)</subfield><subfield code="a">Content-Based Image Retrieval (CBIR)</subfield><subfield code="a">Feature Extraction</subfield><subfield code="a">Magnetic Resonance Image</subfield><subfield code="a">Medical Image Databases</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fatemizadeh, Emad</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Badie, Kambiz</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of healthcare information systems and informatics</subfield><subfield code="d">Hershey, Pa : IGI Global, 2006</subfield><subfield code="g">8(2013), 2, Seite 15-27</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ244419086</subfield><subfield code="w">(DE-600)2242817-3</subfield><subfield code="x">1555-340X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2013</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:15-27</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102</subfield><subfield code="m">X:IGIG</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2013040102&buylink=true</subfield><subfield code="3">Abstract</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-GIS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2013</subfield><subfield code="e">2</subfield><subfield code="h">15-27</subfield></datafield></record></collection>
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