Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis
There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early sta...
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| Veröffentlicht in: | PloS one 2013-10, Vol.8 (10), p.e76880 |
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| description | There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE(-/-) mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p |
| doi_str_mv | 10.1371/journal.pone.0076880 |
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The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE(-/-) mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0076880</identifier><identifier>PMID: 24146940</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Animals ; Apolipoprotein E ; Area Under Curve ; Arteries ; Arteriosclerosis ; Atherosclerosis ; Atherosclerosis - diagnostic imaging ; Atherosclerosis - pathology ; Automation ; Biomedical engineering ; Cardiovascular disease ; Carotid arteries ; Carotid Arteries - diagnostic imaging ; Carotid Arteries - pathology ; Carotid artery ; Classifiers ; Diagnosis ; Disease Models, Animal ; Early Diagnosis ; Engineering ; Feature extraction ; Fractal analysis ; High fat diet ; Identification methods ; Image detection ; Image Processing, Computer-Assisted - methods ; K nearest neighbour classification tree analysis ; K-nearest neighbors algorithm ; Lauterbur, Paul C ; Medical diagnosis ; Methods ; Mice ; Mice, Knockout ; Radio frequency ; ROC Curve ; Sensitivity ; Signal processing ; Spatial analysis ; Statistical analysis ; Statistical methods ; Statistics ; Stroke ; Studies ; Support Vector Machine ; Surface layers ; Surface roughness ; Texture ; Ultrasonic imaging ; Ultrasonography ; Ultrasound</subject><ispartof>PloS one, 2013-10, Vol.8 (10), p.e76880</ispartof><rights>COPYRIGHT 2013 Public Library of Science</rights><rights>2013 Niu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://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>2013 Niu et al 2013 Niu et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-f65ab15b86c9e95b11c809d0a8c01f80cb6ce82302d040db753e4a6786f2038a3</citedby><cites>FETCH-LOGICAL-c758t-f65ab15b86c9e95b11c809d0a8c01f80cb6ce82302d040db753e4a6786f2038a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3798305/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3798305/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24146940$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Yang, Xiaoming</contributor><creatorcontrib>Niu, Lili</creatorcontrib><creatorcontrib>Qian, Ming</creatorcontrib><creatorcontrib>Yang, Wei</creatorcontrib><creatorcontrib>Meng, Long</creatorcontrib><creatorcontrib>Xiao, Yang</creatorcontrib><creatorcontrib>Wong, Kelvin K L</creatorcontrib><creatorcontrib>Abbott, Derek</creatorcontrib><creatorcontrib>Liu, Xin</creatorcontrib><creatorcontrib>Zheng, Hairong</creatorcontrib><title>Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE(-/-) mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Apolipoprotein E</subject><subject>Area Under Curve</subject><subject>Arteries</subject><subject>Arteriosclerosis</subject><subject>Atherosclerosis</subject><subject>Atherosclerosis - diagnostic imaging</subject><subject>Atherosclerosis - pathology</subject><subject>Automation</subject><subject>Biomedical engineering</subject><subject>Cardiovascular disease</subject><subject>Carotid arteries</subject><subject>Carotid Arteries - diagnostic imaging</subject><subject>Carotid Arteries - pathology</subject><subject>Carotid artery</subject><subject>Classifiers</subject><subject>Diagnosis</subject><subject>Disease Models, Animal</subject><subject>Early Diagnosis</subject><subject>Engineering</subject><subject>Feature extraction</subject><subject>Fractal analysis</subject><subject>High fat diet</subject><subject>Identification methods</subject><subject>Image detection</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>K nearest neighbour classification tree analysis</subject><subject>K-nearest neighbors algorithm</subject><subject>Lauterbur, Paul C</subject><subject>Medical diagnosis</subject><subject>Methods</subject><subject>Mice</subject><subject>Mice, Knockout</subject><subject>Radio frequency</subject><subject>ROC Curve</subject><subject>Sensitivity</subject><subject>Signal processing</subject><subject>Spatial analysis</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Stroke</subject><subject>Studies</subject><subject>Support Vector Machine</subject><subject>Surface layers</subject><subject>Surface roughness</subject><subject>Texture</subject><subject>Ultrasonic imaging</subject><subject>Ultrasonography</subject><subject>Ultrasound</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl2L1DAUhoso7jr6D0QLguDFjPluerOwLH4MLCy46m1Ik9NOhk4zJu2y8-9Nne4yBQUJNCV5zpuTN2-WvcZohWmBP279EDrdrva-gxVChZASPcnOcUnJUhBEn578n2UvYtwixKkU4nl2RhhmomToPLu_HUKtDeTBD82mgxhzCz2Y3vku93WuQw_BQczvnM57uO-HALlO5x6iiyMwtH3Q0Q-dzd1ON4msfchBh_aQW6ebzk-g7jcQfDTt-HXxZfas1m2EV9O8yH58_vT96uvy-ubL-uryemkKLvtlLbiuMK-kMCWUvMLYSFRapKVBuJbIVMKAJBQRixiyVcEpMC0KKep0b6npInt71N23PqrJtKgwY0RwzEWZiPWRsF5v1T6ka4SD8tqpPws-NCq54FLjyjJOhOWSWEEYZ6UGIhipamBSpj1IWhfTaUO1A2ugS-60M9H5Tuc2qvF3ihalpOl9Ftm7SSD4XwPE_h8tT1SjU1euq30SMzsXjbpkRbKDkgIlavUXKg0LO2dSbGqX1mcFH2YFiRmfvNFDjGp9--3_2Zufc_b9CbsB3fab6NthDFmcg-wImpSRGKB-dA4jNab-wQ01pl5NqU9lb05dfyx6iDn9DfEn_ug</recordid><startdate>20131017</startdate><enddate>20131017</enddate><creator>Niu, Lili</creator><creator>Qian, Ming</creator><creator>Yang, Wei</creator><creator>Meng, Long</creator><creator>Xiao, Yang</creator><creator>Wong, Kelvin K L</creator><creator>Abbott, Derek</creator><creator>Liu, Xin</creator><creator>Zheng, Hairong</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20131017</creationdate><title>Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis</title><author>Niu, Lili ; Qian, Ming ; Yang, Wei ; Meng, Long ; Xiao, Yang ; Wong, Kelvin K L ; Abbott, Derek ; Liu, Xin ; Zheng, Hairong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c758t-f65ab15b86c9e95b11c809d0a8c01f80cb6ce82302d040db753e4a6786f2038a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Apolipoprotein E</topic><topic>Area Under Curve</topic><topic>Arteries</topic><topic>Arteriosclerosis</topic><topic>Atherosclerosis</topic><topic>Atherosclerosis - diagnostic imaging</topic><topic>Atherosclerosis - pathology</topic><topic>Automation</topic><topic>Biomedical engineering</topic><topic>Cardiovascular disease</topic><topic>Carotid arteries</topic><topic>Carotid Arteries - diagnostic imaging</topic><topic>Carotid Arteries - pathology</topic><topic>Carotid artery</topic><topic>Classifiers</topic><topic>Diagnosis</topic><topic>Disease Models, Animal</topic><topic>Early Diagnosis</topic><topic>Engineering</topic><topic>Feature extraction</topic><topic>Fractal analysis</topic><topic>High fat diet</topic><topic>Identification methods</topic><topic>Image detection</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>K nearest neighbour classification tree analysis</topic><topic>K-nearest neighbors algorithm</topic><topic>Lauterbur, Paul C</topic><topic>Medical diagnosis</topic><topic>Methods</topic><topic>Mice</topic><topic>Mice, Knockout</topic><topic>Radio frequency</topic><topic>ROC Curve</topic><topic>Sensitivity</topic><topic>Signal processing</topic><topic>Spatial analysis</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistics</topic><topic>Stroke</topic><topic>Studies</topic><topic>Support Vector Machine</topic><topic>Surface layers</topic><topic>Surface roughness</topic><topic>Texture</topic><topic>Ultrasonic imaging</topic><topic>Ultrasonography</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Niu, Lili</creatorcontrib><creatorcontrib>Qian, Ming</creatorcontrib><creatorcontrib>Yang, Wei</creatorcontrib><creatorcontrib>Meng, Long</creatorcontrib><creatorcontrib>Xiao, Yang</creatorcontrib><creatorcontrib>Wong, Kelvin K L</creatorcontrib><creatorcontrib>Abbott, Derek</creatorcontrib><creatorcontrib>Liu, Xin</creatorcontrib><creatorcontrib>Zheng, Hairong</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>Nursing & Allied Health Database</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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The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE(-/-) mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>24146940</pmid><doi>10.1371/journal.pone.0076880</doi><tpages>e76880</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Animals Apolipoprotein E Area Under Curve Arteries Arteriosclerosis Atherosclerosis Atherosclerosis - diagnostic imaging Atherosclerosis - pathology Automation Biomedical engineering Cardiovascular disease Carotid arteries Carotid Arteries - diagnostic imaging Carotid Arteries - pathology Carotid artery Classifiers Diagnosis Disease Models, Animal Early Diagnosis Engineering Feature extraction Fractal analysis High fat diet Identification methods Image detection Image Processing, Computer-Assisted - methods K nearest neighbour classification tree analysis K-nearest neighbors algorithm Lauterbur, Paul C Medical diagnosis Methods Mice Mice, Knockout Radio frequency ROC Curve Sensitivity Signal processing Spatial analysis Statistical analysis Statistical methods Statistics Stroke Studies Support Vector Machine Surface layers Surface roughness Texture Ultrasonic imaging Ultrasonography Ultrasound |
| title | Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis |
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