Automatic Segmentation of Dermoscopy Images using Saliency Combined with Otsu Threshold
Abstract Segmentation is one of the crucial steps for the computer-aided diagnosis (CAD) of skin cancer with dermoscopy images. To accurately extract lesion borders from dermoscopy images, a novel automatic segmentation algorithm using saliency combined with Otsu threshold is proposed in this paper,...
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| Veröffentlicht in: | Computers in biology and medicine 2017-06, Vol.85, p.75-85 |
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| creator | Fan, Haidi Xie, Fengying Li, Yang Jiang, Zhiguo Liu, Jie |
| description | Abstract Segmentation is one of the crucial steps for the computer-aided diagnosis (CAD) of skin cancer with dermoscopy images. To accurately extract lesion borders from dermoscopy images, a novel automatic segmentation algorithm using saliency combined with Otsu threshold is proposed in this paper, which includes enhancement and segmentation stages. In the enhancement stage, prior information on healthy skin is extracted, and the color saliency map and brightness saliency map are constructed respectively. By fusing the two saliency maps, the final enhanced image is obtained. In the segmentation stage, according to the histogram distribution of the enhanced image, an optimization function is designed to adjust the traditional Otsu threshold method to obtain more accurate lesion borders. The proposed model is validated from enhancement effectiveness and segmentation accuracy. Experimental results demonstrate that our method is robust and performs better than other state-of-the-art methods. |
| doi_str_mv | 10.1016/j.compbiomed.2017.03.025 |
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To accurately extract lesion borders from dermoscopy images, a novel automatic segmentation algorithm using saliency combined with Otsu threshold is proposed in this paper, which includes enhancement and segmentation stages. In the enhancement stage, prior information on healthy skin is extracted, and the color saliency map and brightness saliency map are constructed respectively. By fusing the two saliency maps, the final enhanced image is obtained. In the segmentation stage, according to the histogram distribution of the enhanced image, an optimization function is designed to adjust the traditional Otsu threshold method to obtain more accurate lesion borders. The proposed model is validated from enhancement effectiveness and segmentation accuracy. Experimental results demonstrate that our method is robust and performs better than other state-of-the-art methods.</description><identifier>ISSN: 0010-4825</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2017.03.025</identifier><identifier>PMID: 28460258</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Accuracy ; Algorithms ; Automatic segmentation ; Automation ; Borders ; Brightness ; CAD ; Cancer ; Cluster Analysis ; Color ; Computer aided design ; Computer-aided diagnosis ; Databases, Factual ; Dermatology ; Dermoscopy - methods ; Dermoscopy images ; Diagnosis ; Genetic algorithms ; Humans ; Image Interpretation, Computer-Assisted - methods ; Image processing ; Image segmentation ; Internal Medicine ; International conferences ; Medical diagnosis ; Melanoma ; Methods ; Microscopy ; Neurosciences ; Optimization ; Other ; Pattern recognition ; Researchers ; Robustness ; Saliency ; Skin cancer ; Skin Neoplasms - diagnostic imaging ; State of the art ; Threshold</subject><ispartof>Computers in biology and medicine, 2017-06, Vol.85, p.75-85</ispartof><rights>2017 Elsevier Ltd</rights><rights>Copyright © 2017 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Elsevier Limited Jun 1, 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c457t-e245c2e3cdd2d0c6a341cbafab421a7b3aca71af87c168b257ebaeef51bdf1a83</citedby><cites>FETCH-LOGICAL-c457t-e245c2e3cdd2d0c6a341cbafab421a7b3aca71af87c168b257ebaeef51bdf1a83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1904826064?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28460258$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fan, Haidi</creatorcontrib><creatorcontrib>Xie, Fengying</creatorcontrib><creatorcontrib>Li, Yang</creatorcontrib><creatorcontrib>Jiang, Zhiguo</creatorcontrib><creatorcontrib>Liu, Jie</creatorcontrib><title>Automatic Segmentation of Dermoscopy Images using Saliency Combined with Otsu Threshold</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Abstract Segmentation is one of the crucial steps for the computer-aided diagnosis (CAD) of skin cancer with dermoscopy images. To accurately extract lesion borders from dermoscopy images, a novel automatic segmentation algorithm using saliency combined with Otsu threshold is proposed in this paper, which includes enhancement and segmentation stages. In the enhancement stage, prior information on healthy skin is extracted, and the color saliency map and brightness saliency map are constructed respectively. By fusing the two saliency maps, the final enhanced image is obtained. In the segmentation stage, according to the histogram distribution of the enhanced image, an optimization function is designed to adjust the traditional Otsu threshold method to obtain more accurate lesion borders. The proposed model is validated from enhancement effectiveness and segmentation accuracy. Experimental results demonstrate that our method is robust and performs better than other state-of-the-art methods.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Automatic segmentation</subject><subject>Automation</subject><subject>Borders</subject><subject>Brightness</subject><subject>CAD</subject><subject>Cancer</subject><subject>Cluster Analysis</subject><subject>Color</subject><subject>Computer aided design</subject><subject>Computer-aided diagnosis</subject><subject>Databases, Factual</subject><subject>Dermatology</subject><subject>Dermoscopy - methods</subject><subject>Dermoscopy images</subject><subject>Diagnosis</subject><subject>Genetic algorithms</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Internal Medicine</subject><subject>International conferences</subject><subject>Medical diagnosis</subject><subject>Melanoma</subject><subject>Methods</subject><subject>Microscopy</subject><subject>Neurosciences</subject><subject>Optimization</subject><subject>Other</subject><subject>Pattern recognition</subject><subject>Researchers</subject><subject>Robustness</subject><subject>Saliency</subject><subject>Skin cancer</subject><subject>Skin Neoplasms - diagnostic imaging</subject><subject>State of the art</subject><subject>Threshold</subject><issn>0010-4825</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNkktr3DAQgEVpabZJ_0IR9NKL3dHDtvZSSDdpEgjksCnpTcjyeFdb29pKdsv--8hsQiCnnkYw3zz0MYRQBjkDVn7d5db3-9r5HpucA6tyEDnw4g1ZMFUtMyiEfEsWAAwyqXhxQj7EuAMACQLekxOuZJlwtSAP59PoezM6S9e46XEY09sP1Lf0AkPvo_X7A73pzQYjnaIbNnRtOoeDPdCV72s3YEP_uXFL78Y40fttwLj1XXNG3rWmi_jxKZ6Snz8u71fX2e3d1c3q_DazsqjGDLksLEdhm4Y3YEsjJLO1aU0tOTNVLYw1FTOtqiwrVc2LCmuD2BasblpmlDglX45998H_mTCOunfRYteZAf0UNVNLWbBloXhCP79Cd34KQ9pOsyUkTSWUMlHqSNngYwzY6n1wvQkHzUDP8vVOv8jXs3wNQiebqfTT04CpnnPPhc-2E_D9CGAy8tdh0NHOKrFxAe2oG-_-Z8q3V01s5wZnTfcbDxhf_qQj16DX8xHMN8AqAaDgl3gEhQexDA</recordid><startdate>20170601</startdate><enddate>20170601</enddate><creator>Fan, Haidi</creator><creator>Xie, Fengying</creator><creator>Li, Yang</creator><creator>Jiang, Zhiguo</creator><creator>Liu, Jie</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><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>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>M7Z</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20170601</creationdate><title>Automatic Segmentation of Dermoscopy Images using Saliency Combined with Otsu Threshold</title><author>Fan, Haidi ; Xie, Fengying ; Li, Yang ; Jiang, Zhiguo ; Liu, Jie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c457t-e245c2e3cdd2d0c6a341cbafab421a7b3aca71af87c168b257ebaeef51bdf1a83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Automatic segmentation</topic><topic>Automation</topic><topic>Borders</topic><topic>Brightness</topic><topic>CAD</topic><topic>Cancer</topic><topic>Cluster Analysis</topic><topic>Color</topic><topic>Computer aided design</topic><topic>Computer-aided diagnosis</topic><topic>Databases, Factual</topic><topic>Dermatology</topic><topic>Dermoscopy - methods</topic><topic>Dermoscopy images</topic><topic>Diagnosis</topic><topic>Genetic algorithms</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Internal Medicine</topic><topic>International conferences</topic><topic>Medical diagnosis</topic><topic>Melanoma</topic><topic>Methods</topic><topic>Microscopy</topic><topic>Neurosciences</topic><topic>Optimization</topic><topic>Other</topic><topic>Pattern recognition</topic><topic>Researchers</topic><topic>Robustness</topic><topic>Saliency</topic><topic>Skin cancer</topic><topic>Skin Neoplasms - diagnostic imaging</topic><topic>State of the art</topic><topic>Threshold</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fan, Haidi</creatorcontrib><creatorcontrib>Xie, Fengying</creatorcontrib><creatorcontrib>Li, Yang</creatorcontrib><creatorcontrib>Jiang, Zhiguo</creatorcontrib><creatorcontrib>Liu, Jie</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical 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 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>Research Library (Alumni Edition)</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>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</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>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</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>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fan, Haidi</au><au>Xie, Fengying</au><au>Li, Yang</au><au>Jiang, Zhiguo</au><au>Liu, Jie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic Segmentation of Dermoscopy Images using Saliency Combined with Otsu Threshold</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2017-06-01</date><risdate>2017</risdate><volume>85</volume><spage>75</spage><epage>85</epage><pages>75-85</pages><issn>0010-4825</issn><eissn>1879-0534</eissn><abstract>Abstract Segmentation is one of the crucial steps for the computer-aided diagnosis (CAD) of skin cancer with dermoscopy images. To accurately extract lesion borders from dermoscopy images, a novel automatic segmentation algorithm using saliency combined with Otsu threshold is proposed in this paper, which includes enhancement and segmentation stages. In the enhancement stage, prior information on healthy skin is extracted, and the color saliency map and brightness saliency map are constructed respectively. By fusing the two saliency maps, the final enhanced image is obtained. In the segmentation stage, according to the histogram distribution of the enhanced image, an optimization function is designed to adjust the traditional Otsu threshold method to obtain more accurate lesion borders. The proposed model is validated from enhancement effectiveness and segmentation accuracy. Experimental results demonstrate that our method is robust and performs better than other state-of-the-art methods.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>28460258</pmid><doi>10.1016/j.compbiomed.2017.03.025</doi><tpages>11</tpages></addata></record> |
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| subjects | Accuracy Algorithms Automatic segmentation Automation Borders Brightness CAD Cancer Cluster Analysis Color Computer aided design Computer-aided diagnosis Databases, Factual Dermatology Dermoscopy - methods Dermoscopy images Diagnosis Genetic algorithms Humans Image Interpretation, Computer-Assisted - methods Image processing Image segmentation Internal Medicine International conferences Medical diagnosis Melanoma Methods Microscopy Neurosciences Optimization Other Pattern recognition Researchers Robustness Saliency Skin cancer Skin Neoplasms - diagnostic imaging State of the art Threshold |
| title | Automatic Segmentation of Dermoscopy Images using Saliency Combined with Otsu Threshold |
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