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Texture Analysis of Supraspinatus Ultrasound Image for Computer Aided Diagnostic System

DC Field Value Language
dc.contributor.author유선국-
dc.contributor.author장원석-
dc.date.accessioned2017-10-26T07:59:05Z-
dc.date.available2017-10-26T07:59:05Z-
dc.date.issued2016-
dc.identifier.issn2093-3681-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/152784-
dc.description.abstractOBJECTIVES: In this paper, we proposed an algorithm for recognizing a rotator cuff supraspinatus tendon tear using a texture analysis based on a histogram, gray level co-occurrence matrix (GLCM), and gray level run length matrix (GLRLM). METHODS: First, we applied a total of 57 features (5 first order descriptors, 40 GLCM features, and 12 GLRLM features) to each rotator cuff region of interest. Our results show that first order statistics (mean, skewness, entropy, energy, smoothness), GLCM (correlation, contrast, energy, entropy, difference entropy, homogeneity, maximum probability, sum average, sum entropy), and GLRLM features are helpful to distinguish a normal supraspinatus tendon and an abnormal supraspinatus tendon. The statistical significance of these features is verified using a t-test. The support vector machine classification showed accuracy using feature combinations. Support Vector Machine offers good performance with a small amount of training data. Sensitivity, specificity, and accuracy are used to evaluate performance of a classification test. RESULTS: From the results, first order statics features and GLCM and GLRLM features afford 95%, 85%, and 100% accuracy, respectively. First order statistics and GLCM and GLRLM features in combination provided 100% accuracy. Combinations that include GLRLM features had high accuracy. GLRLM features were confirmed as highly accurate features for classified normal and abnormal. CONCLUSIONS: This algorithm will be helpful to diagnose supraspinatus tendon tear on ultrasound images.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/octet-stream-
dc.languageKorean-
dc.publisherKorean Society of Medical Informatics-
dc.relation.isPartOfHEALTHCARE INFORMATICS RESEARCH-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.titleTexture Analysis of Supraspinatus Ultrasound Image for Computer Aided Diagnostic System-
dc.typeArticle-
dc.publisher.locationKorea-
dc.contributor.collegeCollege of Medicine-
dc.contributor.departmentDept. of Medical Engineering-
dc.contributor.googleauthorByung Eun Park-
dc.contributor.googleauthorWon Seuk Jang-
dc.contributor.googleauthorSun Kook Yoo-
dc.identifier.doi10.4258/hir.2016.22.4.299-
dc.contributor.localIdA02471-
dc.relation.journalcodeJ00974-
dc.identifier.eissn2093-369X-
dc.relation.journalsince2010~-
dc.identifier.pmid27895962-
dc.relation.journalbefore~2009 Journal of Korean Society of Medical Informatics (대한의료정보학회지)-
dc.subject.keywordComputer-Assisted Image Analysis-
dc.subject.keywordRotator Cuff-
dc.subject.keywordStatistical Data Analyses-
dc.subject.keywordSupport Vector Machine-
dc.subject.keywordUltrasonography-
dc.contributor.alternativeNameYoo, Sun Kook-
dc.contributor.affiliatedAuthorYoo, Sun Kook-
dc.citation.volume22-
dc.citation.number4-
dc.citation.startPage299-
dc.citation.endPage304-
dc.identifier.bibliographicCitationHEALTHCARE INFORMATICS RESEARCH, Vol.22(4) : 299-304, 2016-
dc.date.modified2017-10-24-
dc.identifier.rimsid39792-
dc.type.rimsART-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers

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