{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T14:46:41Z","timestamp":1770475601421,"version":"3.49.0"},"reference-count":19,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2018,6,1]],"date-time":"2018-06-01T00:00:00Z","timestamp":1527811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,7,27]]},"abstract":"<jats:p>Rotator cuff tear is a common cause of shoulder pain and disability. It is especially critical for elderly people since such injuries are strongly related with age and reduce the quality of life due to the shoulder pain and weakness with shoulder flexion and abduction. Ultrasound of the shoulder is widely used in examining the state of rotator cuff but is often criticized as operator-dependent because of the complexity of the shoulder anatomy or anisotropy and lack of intensity contrast from ultrasonographic images. Automatic segmentation of the related tendon tear by computer assisted software will be the answer for that problem. In this paper, we propose a fully automatic extractor of partial\/full thickness tear of rotator cuff tendon with Fuzzy C-Means based quantization for pixel classification and fuzzy stretching for image contrast enhancement. In experiment, our method exhibits sufficient agreement with human expert. For 12 partial thickness tear cases, the sensitivity was 96.5% and specificity was 91.1% whereas the sensitivity was 92.6% and the specificity was 96.4% for 44 full thickness tear cases.<\/jats:p>","DOI":"10.3233\/jifs-169576","type":"journal-article","created":{"date-parts":[[2018,6,1]],"date-time":"2018-06-01T11:52:59Z","timestamp":1527853979000},"page":"149-158","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["A fuzzy C-means quantization based automatic extraction of rotator cuff tendon tears from ultrasound images"],"prefix":"10.1177","volume":"35","author":[{"given":"Kwang Baek","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Silla University, Busan, Korea"}]},{"given":"Yu-Seon","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Pusan National University, Busan, Korea"}]},{"given":"Hyun Jun","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Software Convergence, Cheongju University, Cheongju, Korea"}]},{"given":"Doo Heon","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Computer Games, Yong-In SongDam College, Yongin, Korea"}]},{"given":"Byung Kwan","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, School of Medicine, Pusan National University, Busan, Korea"}]}],"member":"179","published-online":{"date-parts":[[2018,6]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jse.2009.04.006"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.2106\/00004623-200101000-00010"},{"key":"e_1_3_1_4_2","unstructured":"Rotator cuff tear The Korean Orthopedic association Available: http:\/\/www.koa.or.kr\/info\/index127.php."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/1475-925X-13-157"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.4103\/0971-3026.111481"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-009-1561-9"},{"key":"e_1_3_1_8_2","volume-title":"Murray and Murray and Nadel\u2019s textbook of respiratory medicine: 2-volume set","author":"Mason R.J.","year":"2010","unstructured":"MasonR.J., BroaddusV.C., MartinT., KingT.E.Jr, SchraufnagelD., MurrayJ.F. and NadelJ.A.Murray and Murray and Nadel\u2019s textbook of respiratory medicine: 2-volume set, Elsevier Health Sciences, 2010."},{"key":"e_1_3_1_9_2","first-page":"1394","article-title":"Reliability of musculoskeletalultrasound imaging to measure supraspinatus tendon thickness in healthy subjects","volume":"29","author":"Ahmad A.","year":"2017","unstructured":"AhmadA., BandpeiM.A., GilaniS.A., MunawarA., AhmedI. and TanveerF., Reliability of musculoskeletalultrasound imaging to measure supraspinatus tendon thickness in healthy subjects, Journal of PhysicalTherapy Science29 (2017), 1394\u20131398.","journal-title":"Journal of PhysicalTherapy Science"},{"key":"e_1_3_1_10_2","first-page":"829","article-title":"Evaluation ofsupraspinatus muscle tears by ultrasonography and magnetic resonanceimaging in comparison with surgical findings","volume":"44","author":"Abd-ElGawad E.A.","year":"2013","unstructured":"Abd-ElGawadE.A., IbraheemM.A. and FoulyE.H., Evaluation ofsupraspinatus muscle tears by ultrasonography and magnetic resonanceimaging in comparison with surgical findings, Egyptian J RadiolNucl Med44 (2013), 829\u2013834.","journal-title":"Egyptian J RadiolNucl Med"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.10.4526"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2016.05.016"},{"key":"e_1_3_1_13_2","article-title":"Towards predictive diagnosis and management of rotator cuff disease: Usingcurvelet transform for edge detection and segmentation of tissue","author":"Raikar V.P.","year":"2016","unstructured":"RaikarV.P. and KwartowitzD.M., Towards predictive diagnosis and management of rotator cuff disease: Usingcurvelet transform for edge detection and segmentation of tissue, Proc SPIE 9790, Medical Imaging 2016:Ultrasonic Imaging and Tomography, 97901P2016.","journal-title":"Proc SPIE 9790, Medical Imaging 2016:Ultrasonic Imaging and Tomography, 97901P"},{"key":"e_1_3_1_14_2","first-page":"405","article-title":"Extracting fascia and analysis of muscles from ultrasound images with FCM-based quantization technology","volume":"20","author":"Kim K.B.","year":"2010","unstructured":"KimK.B., LeeH.J., SongD.H. and WooY.W., Extracting fascia and analysis of muscles from ultrasound images with FCM-based quantization technology, Neural Network World20 (2010), 405\u2013416.","journal-title":"Neural Network World"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/5892051"},{"key":"e_1_3_1_16_2","unstructured":"GonzalezR.C. and WoodsR.E. 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