A New Fractional-Order Mask for Image Edge Detection Based on Caputo–Fabrizio Fractional-Order Derivative Without Singular Kernel
In this work, we consider the Caputo–Fabrizio fractional-order derivative to generalize the first-order Sobel operator. The resulting fractional mask is used to carry out edge analysis of medical images. The implementation of this method will allow enhancing the study, and the monitoring of diseases...
Gespeichert in:
| Veröffentlicht in: | Circuits, systems, and signal processing systems, and signal processing, 2020-03, Vol.39 (3), p.1419-1448 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1448 |
|---|---|
| container_issue | 3 |
| container_start_page | 1419 |
| container_title | Circuits, systems, and signal processing |
| container_volume | 39 |
| creator | Lavín-Delgado, J. E. Solís-Pérez, J. E. Gómez-Aguilar, J. F. Escobar-Jiménez, R. F. |
| description | In this work, we consider the Caputo–Fabrizio fractional-order derivative to generalize the first-order Sobel operator. The resulting fractional mask is used to carry out edge analysis of medical images. The implementation of this method will allow enhancing the study, and the monitoring of diseases such as breast cancer, benign cyst, and breast calcifications, among others, to properly treat these diseases. The experimental results showed that the proposed operator gives superior performance over conventional integer-order operators because it can detect more edge details feature of the medical images, as well as it is more robust to noise. |
| doi_str_mv | 10.1007/s00034-019-01200-3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2260124739</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2260124739</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-e7077b84c0e833e455b500c6829f7cfe7a0c73f5f7a72546d5f1795f6519d0e13</originalsourceid><addsrcrecordid>eNp9kE1OwzAQRi0EEqVwAVaWWAfGcRwny9IfqCh0AQh2lpuMS0raFDspghUSR-CGnATTIrFAYjEeL973afQIOWRwzADkiQMAHgXAUj8hQMC3SIsJzgKRyGSbtCCUSQAJu98le87NwJNRGrbIe4de4TMdWJ3VRbXQZTC2OVp6qd0jNZWlw7meIu3n_ulhjWuKnmqHOfWfrl42dfX59jHQE1u8FtXfph7aYqXrYoX0rqgfqqam18Vi2pTa0gu0Cyz3yY7RpcODn90mt4P-Tfc8GI3Pht3OKMg4S-sAJUg5SaIMMOEcIyEmAiCLkzA1MjMoNWSSG2GklqGI4lwYJlNhYsHSHJDxNjna9C5t9dSgq9Wsaqy_1KkwjL23SPLUU-GGymzlnEWjlraYa_uiGKhv2WojW3mFai1bcR_im5Dz8GKK9rf6n9QX9guDAA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2260124739</pqid></control><display><type>article</type><title>A New Fractional-Order Mask for Image Edge Detection Based on Caputo–Fabrizio Fractional-Order Derivative Without Singular Kernel</title><source>SpringerLink Journals - AutoHoldings</source><creator>Lavín-Delgado, J. E. ; Solís-Pérez, J. E. ; Gómez-Aguilar, J. F. ; Escobar-Jiménez, R. F.</creator><creatorcontrib>Lavín-Delgado, J. E. ; Solís-Pérez, J. E. ; Gómez-Aguilar, J. F. ; Escobar-Jiménez, R. F.</creatorcontrib><description>In this work, we consider the Caputo–Fabrizio fractional-order derivative to generalize the first-order Sobel operator. The resulting fractional mask is used to carry out edge analysis of medical images. The implementation of this method will allow enhancing the study, and the monitoring of diseases such as breast cancer, benign cyst, and breast calcifications, among others, to properly treat these diseases. The experimental results showed that the proposed operator gives superior performance over conventional integer-order operators because it can detect more edge details feature of the medical images, as well as it is more robust to noise.</description><identifier>ISSN: 0278-081X</identifier><identifier>EISSN: 1531-5878</identifier><identifier>DOI: 10.1007/s00034-019-01200-3</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Breast cancer ; Circuits and Systems ; Diabetic retinopathy ; Edge detection ; Electrical Engineering ; Electronics and Microelectronics ; Engineering ; Image detection ; Image enhancement ; Instrumentation ; Medical imaging ; Signal,Image and Speech Processing</subject><ispartof>Circuits, systems, and signal processing, 2020-03, Vol.39 (3), p.1419-1448</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Circuits, Systems, and Signal Processing is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-e7077b84c0e833e455b500c6829f7cfe7a0c73f5f7a72546d5f1795f6519d0e13</citedby><cites>FETCH-LOGICAL-c319t-e7077b84c0e833e455b500c6829f7cfe7a0c73f5f7a72546d5f1795f6519d0e13</cites><orcidid>0000-0001-9403-3767</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00034-019-01200-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00034-019-01200-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Lavín-Delgado, J. E.</creatorcontrib><creatorcontrib>Solís-Pérez, J. E.</creatorcontrib><creatorcontrib>Gómez-Aguilar, J. F.</creatorcontrib><creatorcontrib>Escobar-Jiménez, R. F.</creatorcontrib><title>A New Fractional-Order Mask for Image Edge Detection Based on Caputo–Fabrizio Fractional-Order Derivative Without Singular Kernel</title><title>Circuits, systems, and signal processing</title><addtitle>Circuits Syst Signal Process</addtitle><description>In this work, we consider the Caputo–Fabrizio fractional-order derivative to generalize the first-order Sobel operator. The resulting fractional mask is used to carry out edge analysis of medical images. The implementation of this method will allow enhancing the study, and the monitoring of diseases such as breast cancer, benign cyst, and breast calcifications, among others, to properly treat these diseases. The experimental results showed that the proposed operator gives superior performance over conventional integer-order operators because it can detect more edge details feature of the medical images, as well as it is more robust to noise.</description><subject>Breast cancer</subject><subject>Circuits and Systems</subject><subject>Diabetic retinopathy</subject><subject>Edge detection</subject><subject>Electrical Engineering</subject><subject>Electronics and Microelectronics</subject><subject>Engineering</subject><subject>Image detection</subject><subject>Image enhancement</subject><subject>Instrumentation</subject><subject>Medical imaging</subject><subject>Signal,Image and Speech Processing</subject><issn>0278-081X</issn><issn>1531-5878</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kE1OwzAQRi0EEqVwAVaWWAfGcRwny9IfqCh0AQh2lpuMS0raFDspghUSR-CGnATTIrFAYjEeL973afQIOWRwzADkiQMAHgXAUj8hQMC3SIsJzgKRyGSbtCCUSQAJu98le87NwJNRGrbIe4de4TMdWJ3VRbXQZTC2OVp6qd0jNZWlw7meIu3n_ulhjWuKnmqHOfWfrl42dfX59jHQE1u8FtXfph7aYqXrYoX0rqgfqqam18Vi2pTa0gu0Cyz3yY7RpcODn90mt4P-Tfc8GI3Pht3OKMg4S-sAJUg5SaIMMOEcIyEmAiCLkzA1MjMoNWSSG2GklqGI4lwYJlNhYsHSHJDxNjna9C5t9dSgq9Wsaqy_1KkwjL23SPLUU-GGymzlnEWjlraYa_uiGKhv2WojW3mFai1bcR_im5Dz8GKK9rf6n9QX9guDAA</recordid><startdate>20200301</startdate><enddate>20200301</enddate><creator>Lavín-Delgado, J. E.</creator><creator>Solís-Pérez, J. E.</creator><creator>Gómez-Aguilar, J. F.</creator><creator>Escobar-Jiménez, R. F.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7SP</scope><scope>7XB</scope><scope>88I</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>S0W</scope><orcidid>https://orcid.org/0000-0001-9403-3767</orcidid></search><sort><creationdate>20200301</creationdate><title>A New Fractional-Order Mask for Image Edge Detection Based on Caputo–Fabrizio Fractional-Order Derivative Without Singular Kernel</title><author>Lavín-Delgado, J. E. ; Solís-Pérez, J. E. ; Gómez-Aguilar, J. F. ; Escobar-Jiménez, R. F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-e7077b84c0e833e455b500c6829f7cfe7a0c73f5f7a72546d5f1795f6519d0e13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Breast cancer</topic><topic>Circuits and Systems</topic><topic>Diabetic retinopathy</topic><topic>Edge detection</topic><topic>Electrical Engineering</topic><topic>Electronics and Microelectronics</topic><topic>Engineering</topic><topic>Image detection</topic><topic>Image enhancement</topic><topic>Instrumentation</topic><topic>Medical imaging</topic><topic>Signal,Image and Speech Processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lavín-Delgado, J. E.</creatorcontrib><creatorcontrib>Solís-Pérez, J. E.</creatorcontrib><creatorcontrib>Gómez-Aguilar, J. F.</creatorcontrib><creatorcontrib>Escobar-Jiménez, R. F.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering 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>Computing Database</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><jtitle>Circuits, systems, and signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lavín-Delgado, J. E.</au><au>Solís-Pérez, J. E.</au><au>Gómez-Aguilar, J. F.</au><au>Escobar-Jiménez, R. F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A New Fractional-Order Mask for Image Edge Detection Based on Caputo–Fabrizio Fractional-Order Derivative Without Singular Kernel</atitle><jtitle>Circuits, systems, and signal processing</jtitle><stitle>Circuits Syst Signal Process</stitle><date>2020-03-01</date><risdate>2020</risdate><volume>39</volume><issue>3</issue><spage>1419</spage><epage>1448</epage><pages>1419-1448</pages><issn>0278-081X</issn><eissn>1531-5878</eissn><abstract>In this work, we consider the Caputo–Fabrizio fractional-order derivative to generalize the first-order Sobel operator. The resulting fractional mask is used to carry out edge analysis of medical images. The implementation of this method will allow enhancing the study, and the monitoring of diseases such as breast cancer, benign cyst, and breast calcifications, among others, to properly treat these diseases. The experimental results showed that the proposed operator gives superior performance over conventional integer-order operators because it can detect more edge details feature of the medical images, as well as it is more robust to noise.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s00034-019-01200-3</doi><tpages>30</tpages><orcidid>https://orcid.org/0000-0001-9403-3767</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0278-081X |
| ispartof | Circuits, systems, and signal processing, 2020-03, Vol.39 (3), p.1419-1448 |
| issn | 0278-081X 1531-5878 |
| language | eng |
| recordid | cdi_proquest_journals_2260124739 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Breast cancer Circuits and Systems Diabetic retinopathy Edge detection Electrical Engineering Electronics and Microelectronics Engineering Image detection Image enhancement Instrumentation Medical imaging Signal,Image and Speech Processing |
| title | A New Fractional-Order Mask for Image Edge Detection Based on Caputo–Fabrizio Fractional-Order Derivative Without Singular Kernel |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T01%3A15%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20New%20Fractional-Order%20Mask%20for%20Image%20Edge%20Detection%20Based%20on%20Caputo%E2%80%93Fabrizio%20Fractional-Order%20Derivative%20Without%20Singular%20Kernel&rft.jtitle=Circuits,%20systems,%20and%20signal%20processing&rft.au=Lav%C3%ADn-Delgado,%20J.%20E.&rft.date=2020-03-01&rft.volume=39&rft.issue=3&rft.spage=1419&rft.epage=1448&rft.pages=1419-1448&rft.issn=0278-081X&rft.eissn=1531-5878&rft_id=info:doi/10.1007/s00034-019-01200-3&rft_dat=%3Cproquest_cross%3E2260124739%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2260124739&rft_id=info:pmid/&rfr_iscdi=true |