Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods

Background Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often th...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Skin research and technology 2013-02, Vol.19 (1), p.e252-e258
Hauptverfasser: Emre Celebi, M., Wen, Quan, Hwang, Sae, Iyatomi, Hitoshi, Schaefer, Gerald
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page e258
container_issue 1
container_start_page e252
container_title Skin research and technology
container_volume 19
creator Emre Celebi, M.
Wen, Quan
Hwang, Sae
Iyatomi, Hitoshi
Schaefer, Gerald
description Background Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel. However, no single thresholding method appears to be robust enough to successfully handle the wide variety of dermoscopy images encountered in clinical practice. Methods In this article, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thres holding methods. Conclusion Experiments on a difficult set of 90 images demonstrate that the proposed method is robust, fast, and accurate when compared to nine state‐of‐the‐art methods.
doi_str_mv 10.1111/j.1600-0846.2012.00636.x
format Article
fullrecord <record><control><sourceid>proquest_24P</sourceid><recordid>TN_cdi_proquest_miscellaneous_1273314036</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2862099831</sourcerecordid><originalsourceid>FETCH-LOGICAL-c5006-4af5d2622e63cea9be6cf17607c217b33ef6df522ce5fbc60bea5d89d34233ae3</originalsourceid><addsrcrecordid>eNqNkM9P2zAUx61p0-iAfwFF4rJLsmc7dhqJywYMkLpOQPkhLpbjvNB0SdzZqdb-93Mo62EnfLHs9_k-P38IiSgkNKwvi4RKgBjGqUwYUJYASC6T9Tsy2hXekxHkkMeZYI975JP3CwAQOeUfyR5jMpNpDiMym6CvbRd9s65EF51hj6YfLuouHFxrvbHLTXTV6mf00Z2vu-fovPPYFk042yqazR36uW3KofID-7kt_QH5UOnG4-Hrvk_uvp_PTi_jyc-Lq9Ovk9iIMHCc6kqUTDKGkhvUeYHSVDSTkBlGs4JzrGRZCcYMiqowEgrUohznJU8Z5xr5Pvm87bt09vcKfa_a2htsGt2hXXlFWcY5TYHLgB7_hy7synVhukDJMc1Z8Bio8ZYyznrvsFJLV7fabRQFNZhXCzUIVoNgNZhXL-bVOkSPXh9YFS2Wu-A_1QE42QJ_6gY3b26sbm9m8uUD8TZe-x7Xu7h2v5TMeCbUw_RCPV3eP1zz6VQJ_heYKKCK</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1268192201</pqid></control><display><type>article</type><title>Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods</title><source>Wiley Open Access</source><creator>Emre Celebi, M. ; Wen, Quan ; Hwang, Sae ; Iyatomi, Hitoshi ; Schaefer, Gerald</creator><creatorcontrib>Emre Celebi, M. ; Wen, Quan ; Hwang, Sae ; Iyatomi, Hitoshi ; Schaefer, Gerald</creatorcontrib><description>Background Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel. However, no single thresholding method appears to be robust enough to successfully handle the wide variety of dermoscopy images encountered in clinical practice. Methods In this article, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thres holding methods. Conclusion Experiments on a difficult set of 90 images demonstrate that the proposed method is robust, fast, and accurate when compared to nine state‐of‐the‐art methods.</description><identifier>ISSN: 0909-752X</identifier><identifier>EISSN: 1600-0846</identifier><identifier>DOI: 10.1111/j.1600-0846.2012.00636.x</identifier><identifier>PMID: 22676490</identifier><language>eng</language><publisher>England: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Dermoscopy - methods ; Diagnosis, Differential ; Humans ; Image Processing, Computer-Assisted - methods ; Markov Chains ; Melanoma - pathology ; Methods ; Neoplasms - pathology ; Pattern Recognition, Automated - methods ; Skin Neoplasms - pathology ; Studies</subject><ispartof>Skin research and technology, 2013-02, Vol.19 (1), p.e252-e258</ispartof><rights>2012 John Wiley &amp; Sons A/S</rights><rights>2012 John Wiley &amp; Sons A/S.</rights><rights>Copyright © 2013 John Wiley &amp; Sons A/S</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5006-4af5d2622e63cea9be6cf17607c217b33ef6df522ce5fbc60bea5d89d34233ae3</citedby><cites>FETCH-LOGICAL-c5006-4af5d2622e63cea9be6cf17607c217b33ef6df522ce5fbc60bea5d89d34233ae3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fj.1600-0846.2012.00636.x$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fj.1600-0846.2012.00636.x$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,11562,27924,27925,45574,45575,46052,46476</link.rule.ids><linktorsrc>$$Uhttps://onlinelibrary.wiley.com/doi/abs/10.1111%2Fj.1600-0846.2012.00636.x$$EView_record_in_Wiley-Blackwell$$FView_record_in_$$GWiley-Blackwell</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22676490$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Emre Celebi, M.</creatorcontrib><creatorcontrib>Wen, Quan</creatorcontrib><creatorcontrib>Hwang, Sae</creatorcontrib><creatorcontrib>Iyatomi, Hitoshi</creatorcontrib><creatorcontrib>Schaefer, Gerald</creatorcontrib><title>Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods</title><title>Skin research and technology</title><addtitle>Skin Res Technol</addtitle><description>Background Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel. However, no single thresholding method appears to be robust enough to successfully handle the wide variety of dermoscopy images encountered in clinical practice. Methods In this article, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thres holding methods. Conclusion Experiments on a difficult set of 90 images demonstrate that the proposed method is robust, fast, and accurate when compared to nine state‐of‐the‐art methods.</description><subject>Algorithms</subject><subject>Dermoscopy - methods</subject><subject>Diagnosis, Differential</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Markov Chains</subject><subject>Melanoma - pathology</subject><subject>Methods</subject><subject>Neoplasms - pathology</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Skin Neoplasms - pathology</subject><subject>Studies</subject><issn>0909-752X</issn><issn>1600-0846</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkM9P2zAUx61p0-iAfwFF4rJLsmc7dhqJywYMkLpOQPkhLpbjvNB0SdzZqdb-93Mo62EnfLHs9_k-P38IiSgkNKwvi4RKgBjGqUwYUJYASC6T9Tsy2hXekxHkkMeZYI975JP3CwAQOeUfyR5jMpNpDiMym6CvbRd9s65EF51hj6YfLuouHFxrvbHLTXTV6mf00Z2vu-fovPPYFk042yqazR36uW3KofID-7kt_QH5UOnG4-Hrvk_uvp_PTi_jyc-Lq9Ovk9iIMHCc6kqUTDKGkhvUeYHSVDSTkBlGs4JzrGRZCcYMiqowEgrUohznJU8Z5xr5Pvm87bt09vcKfa_a2htsGt2hXXlFWcY5TYHLgB7_hy7synVhukDJMc1Z8Bio8ZYyznrvsFJLV7fabRQFNZhXCzUIVoNgNZhXL-bVOkSPXh9YFS2Wu-A_1QE42QJ_6gY3b26sbm9m8uUD8TZe-x7Xu7h2v5TMeCbUw_RCPV3eP1zz6VQJ_heYKKCK</recordid><startdate>201302</startdate><enddate>201302</enddate><creator>Emre Celebi, M.</creator><creator>Wen, Quan</creator><creator>Hwang, Sae</creator><creator>Iyatomi, Hitoshi</creator><creator>Schaefer, Gerald</creator><general>Blackwell Publishing Ltd</general><general>John Wiley &amp; Sons, Inc</general><scope>BSCLL</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>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>201302</creationdate><title>Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods</title><author>Emre Celebi, M. ; Wen, Quan ; Hwang, Sae ; Iyatomi, Hitoshi ; Schaefer, Gerald</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5006-4af5d2622e63cea9be6cf17607c217b33ef6df522ce5fbc60bea5d89d34233ae3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Dermoscopy - methods</topic><topic>Diagnosis, Differential</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Markov Chains</topic><topic>Melanoma - pathology</topic><topic>Methods</topic><topic>Neoplasms - pathology</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Skin Neoplasms - pathology</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Emre Celebi, M.</creatorcontrib><creatorcontrib>Wen, Quan</creatorcontrib><creatorcontrib>Hwang, Sae</creatorcontrib><creatorcontrib>Iyatomi, Hitoshi</creatorcontrib><creatorcontrib>Schaefer, Gerald</creatorcontrib><collection>Istex</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>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Skin research and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Emre Celebi, M.</au><au>Wen, Quan</au><au>Hwang, Sae</au><au>Iyatomi, Hitoshi</au><au>Schaefer, Gerald</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods</atitle><jtitle>Skin research and technology</jtitle><addtitle>Skin Res Technol</addtitle><date>2013-02</date><risdate>2013</risdate><volume>19</volume><issue>1</issue><spage>e252</spage><epage>e258</epage><pages>e252-e258</pages><issn>0909-752X</issn><eissn>1600-0846</eissn><abstract>Background Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel. However, no single thresholding method appears to be robust enough to successfully handle the wide variety of dermoscopy images encountered in clinical practice. Methods In this article, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thres holding methods. Conclusion Experiments on a difficult set of 90 images demonstrate that the proposed method is robust, fast, and accurate when compared to nine state‐of‐the‐art methods.</abstract><cop>England</cop><pub>Blackwell Publishing Ltd</pub><pmid>22676490</pmid><doi>10.1111/j.1600-0846.2012.00636.x</doi><tpages>7</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 0909-752X
ispartof Skin research and technology, 2013-02, Vol.19 (1), p.e252-e258
issn 0909-752X
1600-0846
language eng
recordid cdi_proquest_miscellaneous_1273314036
source Wiley Open Access
subjects Algorithms
Dermoscopy - methods
Diagnosis, Differential
Humans
Image Processing, Computer-Assisted - methods
Markov Chains
Melanoma - pathology
Methods
Neoplasms - pathology
Pattern Recognition, Automated - methods
Skin Neoplasms - pathology
Studies
title Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T17%3A17%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_24P&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Lesion%20Border%20Detection%20in%20Dermoscopy%20Images%20Using%20Ensembles%20of%20Thresholding%20Methods&rft.jtitle=Skin%20research%20and%20technology&rft.au=Emre%20Celebi,%20M.&rft.date=2013-02&rft.volume=19&rft.issue=1&rft.spage=e252&rft.epage=e258&rft.pages=e252-e258&rft.issn=0909-752X&rft.eissn=1600-0846&rft_id=info:doi/10.1111/j.1600-0846.2012.00636.x&rft_dat=%3Cproquest_24P%3E2862099831%3C/proquest_24P%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1268192201&rft_id=info:pmid/22676490&rfr_iscdi=true