Cited 28 times in
Texture Analysis of Supraspinatus Ultrasound Image for Computer Aided Diagnostic System
DC Field | Value | Language |
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dc.contributor.author | 유선국 | - |
dc.contributor.author | 장원석 | - |
dc.date.accessioned | 2017-10-26T07:59:05Z | - |
dc.date.available | 2017-10-26T07:59:05Z | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 2093-3681 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/152784 | - |
dc.description.abstract | OBJECTIVES: 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.statementOfResponsibility | open | - |
dc.format | application/octet-stream | - |
dc.language | Korean | - |
dc.publisher | Korean Society of Medical Informatics | - |
dc.relation.isPartOf | HEALTHCARE INFORMATICS RESEARCH | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.title | Texture Analysis of Supraspinatus Ultrasound Image for Computer Aided Diagnostic System | - |
dc.type | Article | - |
dc.publisher.location | Korea | - |
dc.contributor.college | College of Medicine | - |
dc.contributor.department | Dept. of Medical Engineering | - |
dc.contributor.googleauthor | Byung Eun Park | - |
dc.contributor.googleauthor | Won Seuk Jang | - |
dc.contributor.googleauthor | Sun Kook Yoo | - |
dc.identifier.doi | 10.4258/hir.2016.22.4.299 | - |
dc.contributor.localId | A02471 | - |
dc.relation.journalcode | J00974 | - |
dc.identifier.eissn | 2093-369X | - |
dc.relation.journalsince | 2010~ | - |
dc.identifier.pmid | 27895962 | - |
dc.relation.journalbefore | ~2009 Journal of Korean Society of Medical Informatics (대한의료정보학회지) | - |
dc.subject.keyword | Computer-Assisted Image Analysis | - |
dc.subject.keyword | Rotator Cuff | - |
dc.subject.keyword | Statistical Data Analyses | - |
dc.subject.keyword | Support Vector Machine | - |
dc.subject.keyword | Ultrasonography | - |
dc.contributor.alternativeName | Yoo, Sun Kook | - |
dc.contributor.affiliatedAuthor | Yoo, Sun Kook | - |
dc.citation.volume | 22 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 299 | - |
dc.citation.endPage | 304 | - |
dc.identifier.bibliographicCitation | HEALTHCARE INFORMATICS RESEARCH, Vol.22(4) : 299-304, 2016 | - |
dc.date.modified | 2017-10-24 | - |
dc.identifier.rimsid | 39792 | - |
dc.type.rims | ART | - |
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