Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images
Asymmetry, color variegation and diameter are considered strong indicators of malignant melanoma. The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having a diameter less than 6mm, deem it necessary to develop an ob...
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| Veröffentlicht in: | PloS one 2020-06, Vol.15 (6), p.e0234352-e0234352 |
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| description | Asymmetry, color variegation and diameter are considered strong indicators of malignant melanoma. The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having a diameter less than 6mm, deem it necessary to develop an objective computer vision system to evaluate these criteria and aid in the early detection of melanoma which could eventually lead to a higher 5-year survival rate. This paper proposes an approach for evaluating the three criteria objectively, whereby we develop a measure to find asymmetry with the aid of a decision tree which we train on the extracted asymmetry measures and then use to predict the asymmetry of new skin lesion images. A range of colors that demonstrate the suspicious colors for the color variegation feature have been derived, and Feret's diameter has been utilized to find the diameter of the skin lesion. The decision tree is 80% accurate in determining the asymmetry of skin lesions, and the number of suspicious colors and diameter values are objectively identified. |
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The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having a diameter less than 6mm, deem it necessary to develop an objective computer vision system to evaluate these criteria and aid in the early detection of melanoma which could eventually lead to a higher 5-year survival rate. This paper proposes an approach for evaluating the three criteria objectively, whereby we develop a measure to find asymmetry with the aid of a decision tree which we train on the extracted asymmetry measures and then use to predict the asymmetry of new skin lesion images. A range of colors that demonstrate the suspicious colors for the color variegation feature have been derived, and Feret's diameter has been utilized to find the diameter of the skin lesion. The decision tree is 80% accurate in determining the asymmetry of skin lesions, and the number of suspicious colors and diameter values are objectively identified.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0234352</identifier><identifier>PMID: 32544197</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Asymmetry ; Causes of ; Color ; Complications and side effects ; Computer science ; Computer vision ; Decision Making, Computer-Assisted ; Decision trees ; Dermatology ; Dermoscopy - methods ; Diagnosis ; Diameters ; Engineering and Technology ; Evaluation ; Health aspects ; Humans ; Identification and classification ; Image processing ; Image Processing, Computer-Assisted - methods ; Lesions ; Medical diagnosis ; Medicine and Health Sciences ; Melanoma ; Melanoma - diagnosis ; Melanoma - diagnostic imaging ; Melanoma - pathology ; Melanoma, Cutaneous Malignant ; Physical Sciences ; Research and Analysis Methods ; Skewness ; Skin cancer ; Skin Diseases ; Skin lesions ; Skin Neoplasms - diagnosis ; Skin Neoplasms - diagnostic imaging ; Skin Neoplasms - pathology ; Survival ; Variegation ; Vision systems</subject><ispartof>PloS one, 2020-06, Vol.15 (6), p.e0234352-e0234352</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Ali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Ali et al 2020 Ali et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-87c61bb504c219277210da4a3d42b075facdf10966de53e0eb764866dc02c6f03</citedby><cites>FETCH-LOGICAL-c692t-87c61bb504c219277210da4a3d42b075facdf10966de53e0eb764866dc02c6f03</cites><orcidid>0000-0002-6758-0084 ; 0000-0002-5450-5472</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297317/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297317/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32544197$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Haass, Nikolas K.</contributor><creatorcontrib>Ali, Abder-Rahman</creatorcontrib><creatorcontrib>Li, Jingpeng</creatorcontrib><creatorcontrib>O'Shea, Sally Jane</creatorcontrib><title>Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Asymmetry, color variegation and diameter are considered strong indicators of malignant melanoma. The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having a diameter less than 6mm, deem it necessary to develop an objective computer vision system to evaluate these criteria and aid in the early detection of melanoma which could eventually lead to a higher 5-year survival rate. This paper proposes an approach for evaluating the three criteria objectively, whereby we develop a measure to find asymmetry with the aid of a decision tree which we train on the extracted asymmetry measures and then use to predict the asymmetry of new skin lesion images. A range of colors that demonstrate the suspicious colors for the color variegation feature have been derived, and Feret's diameter has been utilized to find the diameter of the skin lesion. The decision tree is 80% accurate in determining the asymmetry of skin lesions, and the number of suspicious colors and diameter values are objectively identified.</description><subject>Algorithms</subject><subject>Asymmetry</subject><subject>Causes of</subject><subject>Color</subject><subject>Complications and side effects</subject><subject>Computer science</subject><subject>Computer vision</subject><subject>Decision Making, Computer-Assisted</subject><subject>Decision trees</subject><subject>Dermatology</subject><subject>Dermoscopy - methods</subject><subject>Diagnosis</subject><subject>Diameters</subject><subject>Engineering and Technology</subject><subject>Evaluation</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Identification and classification</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Lesions</subject><subject>Medical diagnosis</subject><subject>Medicine and Health Sciences</subject><subject>Melanoma</subject><subject>Melanoma - 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methods</topic><topic>Diagnosis</topic><topic>Diameters</topic><topic>Engineering and Technology</topic><topic>Evaluation</topic><topic>Health aspects</topic><topic>Humans</topic><topic>Identification and classification</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Lesions</topic><topic>Medical diagnosis</topic><topic>Medicine and Health Sciences</topic><topic>Melanoma</topic><topic>Melanoma - diagnosis</topic><topic>Melanoma - diagnostic imaging</topic><topic>Melanoma - pathology</topic><topic>Melanoma, Cutaneous Malignant</topic><topic>Physical Sciences</topic><topic>Research and Analysis Methods</topic><topic>Skewness</topic><topic>Skin cancer</topic><topic>Skin Diseases</topic><topic>Skin lesions</topic><topic>Skin Neoplasms - diagnosis</topic><topic>Skin Neoplasms - diagnostic imaging</topic><topic>Skin Neoplasms - pathology</topic><topic>Survival</topic><topic>Variegation</topic><topic>Vision systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ali, Abder-Rahman</creatorcontrib><creatorcontrib>Li, Jingpeng</creatorcontrib><creatorcontrib>O'Shea, Sally Jane</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Proquest Nursing & Allied Health Source</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</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>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ali, Abder-Rahman</au><au>Li, Jingpeng</au><au>O'Shea, Sally Jane</au><au>Haass, Nikolas K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-06-16</date><risdate>2020</risdate><volume>15</volume><issue>6</issue><spage>e0234352</spage><epage>e0234352</epage><pages>e0234352-e0234352</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Asymmetry, color variegation and diameter are considered strong indicators of malignant melanoma. The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having a diameter less than 6mm, deem it necessary to develop an objective computer vision system to evaluate these criteria and aid in the early detection of melanoma which could eventually lead to a higher 5-year survival rate. This paper proposes an approach for evaluating the three criteria objectively, whereby we develop a measure to find asymmetry with the aid of a decision tree which we train on the extracted asymmetry measures and then use to predict the asymmetry of new skin lesion images. A range of colors that demonstrate the suspicious colors for the color variegation feature have been derived, and Feret's diameter has been utilized to find the diameter of the skin lesion. The decision tree is 80% accurate in determining the asymmetry of skin lesions, and the number of suspicious colors and diameter values are objectively identified.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32544197</pmid><doi>10.1371/journal.pone.0234352</doi><tpages>e0234352</tpages><orcidid>https://orcid.org/0000-0002-6758-0084</orcidid><orcidid>https://orcid.org/0000-0002-5450-5472</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Asymmetry Causes of Color Complications and side effects Computer science Computer vision Decision Making, Computer-Assisted Decision trees Dermatology Dermoscopy - methods Diagnosis Diameters Engineering and Technology Evaluation Health aspects Humans Identification and classification Image processing Image Processing, Computer-Assisted - methods Lesions Medical diagnosis Medicine and Health Sciences Melanoma Melanoma - diagnosis Melanoma - diagnostic imaging Melanoma - pathology Melanoma, Cutaneous Malignant Physical Sciences Research and Analysis Methods Skewness Skin cancer Skin Diseases Skin lesions Skin Neoplasms - diagnosis Skin Neoplasms - diagnostic imaging Skin Neoplasms - pathology Survival Variegation Vision systems |
| title | Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images |
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