A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM) in ultrasound images
Trigger finger has become a prevalent disease that greatly affects occupational activity and daily life. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint's...
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| Veröffentlicht in: | PloS one 2017-10, Vol.12 (10), p.e0187042-e0187042 |
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| creator | Chuang, Bo-I Kuo, Li-Chieh Yang, Tai-Hua Su, Fong-Chin Jou, I-Ming Lin, Wei-Jr Sun, Yung-Nien |
| description | Trigger finger has become a prevalent disease that greatly affects occupational activity and daily life. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint's tendon structure. In this study, an adaptive texture-based active shape model method is used for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions. The pathology is then determined according to the wavelet and co-occurrence texture features of the segmented tendon area. In the experiments, the segmentation results have fewer errors, with respect to the ground truth, than contours drawn by regular users. The mean values of the absolute segmentation difference of the tendon and synovial sheath are 3.14 and 4.54 pixels, respectively. The average accuracy of pathological determination is 87.14%. The segmentation results are all acceptable in data of both clear and fuzzy boundary cases in 74 images. And the symptom classifications of 42 cases are also a good reference for diagnosis according to the expert clinicians' opinions. |
| doi_str_mv | 10.1371/journal.pone.0187042 |
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Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint's tendon structure. In this study, an adaptive texture-based active shape model method is used for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions. The pathology is then determined according to the wavelet and co-occurrence texture features of the segmented tendon area. In the experiments, the segmentation results have fewer errors, with respect to the ground truth, than contours drawn by regular users. The mean values of the absolute segmentation difference of the tendon and synovial sheath are 3.14 and 4.54 pixels, respectively. The average accuracy of pathological determination is 87.14%. The segmentation results are all acceptable in data of both clear and fuzzy boundary cases in 74 images. And the symptom classifications of 42 cases are also a good reference for diagnosis according to the expert clinicians' opinions.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0187042</identifier><identifier>PMID: 29077737</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adaptive systems ; Artificial intelligence ; Atherosclerosis ; Bioengineering ; Biomedical engineering ; Care and treatment ; Classification ; Computer science ; Diabetic neuropathy ; Diagnosis ; Diagnostic ultrasonography ; Engineering ; Finger ; Ground truth ; Humans ; Image processing ; Image retrieval ; Image segmentation ; International conferences ; Medical imaging ; Models, Anatomic ; Principal components analysis ; Remote sensing ; Studies ; Tenosynovitis ; Texture ; Trigger Finger Disorder - diagnostic imaging ; Ultrasonic imaging ; Ultrasonography - methods ; Ultrasound ; Wavelet transforms</subject><ispartof>PloS one, 2017-10, Vol.12 (10), p.e0187042-e0187042</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Chuang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://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>2017 Chuang et al 2017 Chuang et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5082-faffb1f11ead26d1971b8abc3d1d347e3377648369ff13bac6ae46d448a5765a3</citedby><cites>FETCH-LOGICAL-c5082-faffb1f11ead26d1971b8abc3d1d347e3377648369ff13bac6ae46d448a5765a3</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/PMC5659776/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5659776/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2100,2926,23864,27922,27923,53789,53791,79370,79371</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29077737$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Wang, Yuanquan</contributor><creatorcontrib>Chuang, Bo-I</creatorcontrib><creatorcontrib>Kuo, Li-Chieh</creatorcontrib><creatorcontrib>Yang, Tai-Hua</creatorcontrib><creatorcontrib>Su, Fong-Chin</creatorcontrib><creatorcontrib>Jou, I-Ming</creatorcontrib><creatorcontrib>Lin, Wei-Jr</creatorcontrib><creatorcontrib>Sun, Yung-Nien</creatorcontrib><title>A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM) in ultrasound images</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Trigger finger has become a prevalent disease that greatly affects occupational activity and daily life. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint's tendon structure. In this study, an adaptive texture-based active shape model method is used for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions. The pathology is then determined according to the wavelet and co-occurrence texture features of the segmented tendon area. In the experiments, the segmentation results have fewer errors, with respect to the ground truth, than contours drawn by regular users. The mean values of the absolute segmentation difference of the tendon and synovial sheath are 3.14 and 4.54 pixels, respectively. The average accuracy of pathological determination is 87.14%. The segmentation results are all acceptable in data of both clear and fuzzy boundary cases in 74 images. And the symptom classifications of 42 cases are also a good reference for diagnosis according to the expert clinicians' opinions.</description><subject>Adaptive systems</subject><subject>Artificial intelligence</subject><subject>Atherosclerosis</subject><subject>Bioengineering</subject><subject>Biomedical engineering</subject><subject>Care and treatment</subject><subject>Classification</subject><subject>Computer science</subject><subject>Diabetic neuropathy</subject><subject>Diagnosis</subject><subject>Diagnostic ultrasonography</subject><subject>Engineering</subject><subject>Finger</subject><subject>Ground truth</subject><subject>Humans</subject><subject>Image processing</subject><subject>Image retrieval</subject><subject>Image segmentation</subject><subject>International conferences</subject><subject>Medical imaging</subject><subject>Models, Anatomic</subject><subject>Principal components analysis</subject><subject>Remote sensing</subject><subject>Studies</subject><subject>Tenosynovitis</subject><subject>Texture</subject><subject>Trigger Finger Disorder - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chuang, Bo-I</au><au>Kuo, Li-Chieh</au><au>Yang, Tai-Hua</au><au>Su, Fong-Chin</au><au>Jou, I-Ming</au><au>Lin, Wei-Jr</au><au>Sun, Yung-Nien</au><au>Wang, Yuanquan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM) in ultrasound images</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-10-27</date><risdate>2017</risdate><volume>12</volume><issue>10</issue><spage>e0187042</spage><epage>e0187042</epage><pages>e0187042-e0187042</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Trigger finger has become a prevalent disease that greatly affects occupational activity and daily life. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint's tendon structure. In this study, an adaptive texture-based active shape model method is used for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions. The pathology is then determined according to the wavelet and co-occurrence texture features of the segmented tendon area. In the experiments, the segmentation results have fewer errors, with respect to the ground truth, than contours drawn by regular users. The mean values of the absolute segmentation difference of the tendon and synovial sheath are 3.14 and 4.54 pixels, respectively. The average accuracy of pathological determination is 87.14%. The segmentation results are all acceptable in data of both clear and fuzzy boundary cases in 74 images. And the symptom classifications of 42 cases are also a good reference for diagnosis according to the expert clinicians' opinions.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>29077737</pmid><doi>10.1371/journal.pone.0187042</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Adaptive systems Artificial intelligence Atherosclerosis Bioengineering Biomedical engineering Care and treatment Classification Computer science Diabetic neuropathy Diagnosis Diagnostic ultrasonography Engineering Finger Ground truth Humans Image processing Image retrieval Image segmentation International conferences Medical imaging Models, Anatomic Principal components analysis Remote sensing Studies Tenosynovitis Texture Trigger Finger Disorder - diagnostic imaging Ultrasonic imaging Ultrasonography - methods Ultrasound Wavelet transforms |
| title | A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM) in ultrasound images |
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