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...
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Veröffentlicht in: | Skin research and technology 2013-02, Vol.19 (1), p.e252-e258 |
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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 |
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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 & Sons A/S</rights><rights>2012 John Wiley & Sons A/S.</rights><rights>Copyright © 2013 John Wiley & 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 & 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 & 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> |
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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 |
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