Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE
The accuracy of the fibrotic plaque segmentation is vital in identifying the coronary artery stenosis. In this paper, we address an automated approach (APDE-GMM) for separating the fibrotic plaque area of intravascular optical coherence tomography (IV-OCT) images. Under this approach, an objective f...
Gespeichert in:
| Veröffentlicht in: | Signal, image and video processing image and video processing, 2020-02, Vol.14 (1), p.29-37 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 37 |
|---|---|
| container_issue | 1 |
| container_start_page | 29 |
| container_title | Signal, image and video processing |
| container_volume | 14 |
| creator | Wang, Pengyu Zhu, Hongqing Ling, Xiaofeng |
| description | The accuracy of the fibrotic plaque segmentation is vital in identifying the coronary artery stenosis. In this paper, we address an automated approach (APDE-GMM) for separating the fibrotic plaque area of intravascular optical coherence tomography (IV-OCT) images. Under this approach, an objective function consisting of a new energy functional with Rayleigh distribution and the negative log-likelihood function of Gaussian mixture model (GMM) is developed. Also, the study presents an adaptive diffusivity function where the gradient threshold can be associated to suppress the effect of speckle noise. The parameter estimation is carried out by the expectation–maximization technology. In addition, this paper derives a fourth-order partial differential equation (PDE) via Euler–Lagrange equation to obtain the optimal solutions. It has been compared to other segmentation approaches on synthetic and clinical IV-OCT images. The results demonstrate that APDE-GMM segmentates more accurately. |
| doi_str_mv | 10.1007/s11760-019-01520-6 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2346493893</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2346493893</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-76fcb4591dcab08a547e06ac90adeff4b1f56bde4c89d12210373d42809b1d6e3</originalsourceid><addsrcrecordid>eNp9UMFOwzAMrRBITGM_wCkS50LStGl7RGMMpElwgHPkJm7XqW1Gkk5M_DyBIrhhyfI7vPdsvyi6ZPSaUZrfOMZyQWPKytBZQmNxEs1YIXjMcsZOfzHl59HCuR0NxZO8EMUs-ngcvIUDODV2YInZ-1ZBR5TZosVBIfGmN42F_fZI2h4aJA6bHgcPvjUDqcChJgGsYXSuhYH07bsfLZLeaOwIDJqAhuB6QFKb0fptbKxGS57vVhfRWQ2dw8XPnEev96uX5UO8eVo_Lm83seKs9HEualWlWcm0gooWkKU5UgGqpKCxrtOK1ZmoNKaqKDVLEkZ5znWaFLSsmBbI59HV5Lu35m1E5-UuXDKElTLhqUhLXpQ8sJKJpaxxzmIt9zZ8bI-SUfmVs5xyliFn-Z2zFEHEJ5EL5KFB-2f9j-oTmRGCtg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2346493893</pqid></control><display><type>article</type><title>Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE</title><source>SpringerNature Journals</source><creator>Wang, Pengyu ; Zhu, Hongqing ; Ling, Xiaofeng</creator><creatorcontrib>Wang, Pengyu ; Zhu, Hongqing ; Ling, Xiaofeng</creatorcontrib><description>The accuracy of the fibrotic plaque segmentation is vital in identifying the coronary artery stenosis. In this paper, we address an automated approach (APDE-GMM) for separating the fibrotic plaque area of intravascular optical coherence tomography (IV-OCT) images. Under this approach, an objective function consisting of a new energy functional with Rayleigh distribution and the negative log-likelihood function of Gaussian mixture model (GMM) is developed. Also, the study presents an adaptive diffusivity function where the gradient threshold can be associated to suppress the effect of speckle noise. The parameter estimation is carried out by the expectation–maximization technology. In addition, this paper derives a fourth-order partial differential equation (PDE) via Euler–Lagrange equation to obtain the optimal solutions. It has been compared to other segmentation approaches on synthetic and clinical IV-OCT images. The results demonstrate that APDE-GMM segmentates more accurately.</description><identifier>ISSN: 1863-1703</identifier><identifier>EISSN: 1863-1711</identifier><identifier>DOI: 10.1007/s11760-019-01520-6</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Computer Imaging ; Computer Science ; Energy distribution ; Euler-Lagrange equation ; Fibrosis ; Image Processing and Computer Vision ; Image segmentation ; Model accuracy ; Multimedia Information Systems ; Optical Coherence Tomography ; Optimization ; Original Paper ; Parameter estimation ; Partial differential equations ; Pattern Recognition and Graphics ; Probabilistic models ; Rayleigh distribution ; Signal,Image and Speech Processing ; Tomography ; Vision</subject><ispartof>Signal, image and video processing, 2020-02, Vol.14 (1), p.29-37</ispartof><rights>Springer-Verlag London Ltd., part of Springer Nature 2019</rights><rights>2019© Springer-Verlag London Ltd., part of Springer Nature 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-76fcb4591dcab08a547e06ac90adeff4b1f56bde4c89d12210373d42809b1d6e3</citedby><cites>FETCH-LOGICAL-c319t-76fcb4591dcab08a547e06ac90adeff4b1f56bde4c89d12210373d42809b1d6e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11760-019-01520-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11760-019-01520-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Wang, Pengyu</creatorcontrib><creatorcontrib>Zhu, Hongqing</creatorcontrib><creatorcontrib>Ling, Xiaofeng</creatorcontrib><title>Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE</title><title>Signal, image and video processing</title><addtitle>SIViP</addtitle><description>The accuracy of the fibrotic plaque segmentation is vital in identifying the coronary artery stenosis. In this paper, we address an automated approach (APDE-GMM) for separating the fibrotic plaque area of intravascular optical coherence tomography (IV-OCT) images. Under this approach, an objective function consisting of a new energy functional with Rayleigh distribution and the negative log-likelihood function of Gaussian mixture model (GMM) is developed. Also, the study presents an adaptive diffusivity function where the gradient threshold can be associated to suppress the effect of speckle noise. The parameter estimation is carried out by the expectation–maximization technology. In addition, this paper derives a fourth-order partial differential equation (PDE) via Euler–Lagrange equation to obtain the optimal solutions. It has been compared to other segmentation approaches on synthetic and clinical IV-OCT images. The results demonstrate that APDE-GMM segmentates more accurately.</description><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Energy distribution</subject><subject>Euler-Lagrange equation</subject><subject>Fibrosis</subject><subject>Image Processing and Computer Vision</subject><subject>Image segmentation</subject><subject>Model accuracy</subject><subject>Multimedia Information Systems</subject><subject>Optical Coherence Tomography</subject><subject>Optimization</subject><subject>Original Paper</subject><subject>Parameter estimation</subject><subject>Partial differential equations</subject><subject>Pattern Recognition and Graphics</subject><subject>Probabilistic models</subject><subject>Rayleigh distribution</subject><subject>Signal,Image and Speech Processing</subject><subject>Tomography</subject><subject>Vision</subject><issn>1863-1703</issn><issn>1863-1711</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9UMFOwzAMrRBITGM_wCkS50LStGl7RGMMpElwgHPkJm7XqW1Gkk5M_DyBIrhhyfI7vPdsvyi6ZPSaUZrfOMZyQWPKytBZQmNxEs1YIXjMcsZOfzHl59HCuR0NxZO8EMUs-ngcvIUDODV2YInZ-1ZBR5TZosVBIfGmN42F_fZI2h4aJA6bHgcPvjUDqcChJgGsYXSuhYH07bsfLZLeaOwIDJqAhuB6QFKb0fptbKxGS57vVhfRWQ2dw8XPnEev96uX5UO8eVo_Lm83seKs9HEualWlWcm0gooWkKU5UgGqpKCxrtOK1ZmoNKaqKDVLEkZ5znWaFLSsmBbI59HV5Lu35m1E5-UuXDKElTLhqUhLXpQ8sJKJpaxxzmIt9zZ8bI-SUfmVs5xyliFn-Z2zFEHEJ5EL5KFB-2f9j-oTmRGCtg</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Wang, Pengyu</creator><creator>Zhu, Hongqing</creator><creator>Ling, Xiaofeng</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20200201</creationdate><title>Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE</title><author>Wang, Pengyu ; Zhu, Hongqing ; Ling, Xiaofeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-76fcb4591dcab08a547e06ac90adeff4b1f56bde4c89d12210373d42809b1d6e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Energy distribution</topic><topic>Euler-Lagrange equation</topic><topic>Fibrosis</topic><topic>Image Processing and Computer Vision</topic><topic>Image segmentation</topic><topic>Model accuracy</topic><topic>Multimedia Information Systems</topic><topic>Optical Coherence Tomography</topic><topic>Optimization</topic><topic>Original Paper</topic><topic>Parameter estimation</topic><topic>Partial differential equations</topic><topic>Pattern Recognition and Graphics</topic><topic>Probabilistic models</topic><topic>Rayleigh distribution</topic><topic>Signal,Image and Speech Processing</topic><topic>Tomography</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Pengyu</creatorcontrib><creatorcontrib>Zhu, Hongqing</creatorcontrib><creatorcontrib>Ling, Xiaofeng</creatorcontrib><collection>CrossRef</collection><jtitle>Signal, image and video processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Pengyu</au><au>Zhu, Hongqing</au><au>Ling, Xiaofeng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE</atitle><jtitle>Signal, image and video processing</jtitle><stitle>SIViP</stitle><date>2020-02-01</date><risdate>2020</risdate><volume>14</volume><issue>1</issue><spage>29</spage><epage>37</epage><pages>29-37</pages><issn>1863-1703</issn><eissn>1863-1711</eissn><abstract>The accuracy of the fibrotic plaque segmentation is vital in identifying the coronary artery stenosis. In this paper, we address an automated approach (APDE-GMM) for separating the fibrotic plaque area of intravascular optical coherence tomography (IV-OCT) images. Under this approach, an objective function consisting of a new energy functional with Rayleigh distribution and the negative log-likelihood function of Gaussian mixture model (GMM) is developed. Also, the study presents an adaptive diffusivity function where the gradient threshold can be associated to suppress the effect of speckle noise. The parameter estimation is carried out by the expectation–maximization technology. In addition, this paper derives a fourth-order partial differential equation (PDE) via Euler–Lagrange equation to obtain the optimal solutions. It has been compared to other segmentation approaches on synthetic and clinical IV-OCT images. The results demonstrate that APDE-GMM segmentates more accurately.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s11760-019-01520-6</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1863-1703 |
| ispartof | Signal, image and video processing, 2020-02, Vol.14 (1), p.29-37 |
| issn | 1863-1703 1863-1711 |
| language | eng |
| recordid | cdi_proquest_journals_2346493893 |
| source | SpringerNature Journals |
| subjects | Computer Imaging Computer Science Energy distribution Euler-Lagrange equation Fibrosis Image Processing and Computer Vision Image segmentation Model accuracy Multimedia Information Systems Optical Coherence Tomography Optimization Original Paper Parameter estimation Partial differential equations Pattern Recognition and Graphics Probabilistic models Rayleigh distribution Signal,Image and Speech Processing Tomography Vision |
| title | Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T14%3A56%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Intravascular%20optical%20coherence%20tomography%20image%20segmentation%20based%20on%20Gaussian%20mixture%20model%20and%20adaptive%20fourth-order%20PDE&rft.jtitle=Signal,%20image%20and%20video%20processing&rft.au=Wang,%20Pengyu&rft.date=2020-02-01&rft.volume=14&rft.issue=1&rft.spage=29&rft.epage=37&rft.pages=29-37&rft.issn=1863-1703&rft.eissn=1863-1711&rft_id=info:doi/10.1007/s11760-019-01520-6&rft_dat=%3Cproquest_cross%3E2346493893%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2346493893&rft_id=info:pmid/&rfr_iscdi=true |