Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms
This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest pat...
Saved in:
| Published in: | IEEE transactions on biomedical engineering Vol. 65; no. 5; pp. 989 - 1001 |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
IEEE
01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0018-9294, 1558-2531, 1558-2531 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image g is transformed into three sets: T (true), I (indeterminate) that represents noise, and F (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set I, and a new λ-correction operation is introduced to compute the set T in neutrosophic domain. Second, a graph shortestpath method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights. Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively. |
|---|---|
| AbstractList | This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image g is transformed into three sets: T (true), I (indeterminate) that represents noise, and F (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set I, and a new λ-correction operation is introduced to compute the set T in neutrosophic domain. Second, a graph shortestpath method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights. Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively. This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image is transformed into three sets: (true), (indeterminate) that represents noise, and (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set , and a new -correction operation is introduced to compute the set in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights . Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively.This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image is transformed into three sets: (true), (indeterminate) that represents noise, and (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set , and a new -correction operation is introduced to compute the set in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights . Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively. This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image is transformed into three sets: (true), (indeterminate) that represents noise, and (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set , and a new -correction operation is introduced to compute the set in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights . Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively. |
| Author | Parhi, Keshab K. Koozekanani, Dara D. Sadri, Saeed Nazari, Behzad Drayna, Paul M. Rashno, Abdolreza Rabbani, Hossein |
| Author_xml | – sequence: 1 givenname: Abdolreza surname: Rashno fullname: Rashno, Abdolreza organization: Isfahan University of Technology and also with the University of Minnesota – sequence: 2 givenname: Dara D. surname: Koozekanani fullname: Koozekanani, Dara D. organization: Department of Ophthalmology and Visual NeurosciencesUniversity of Minnesota – sequence: 3 givenname: Paul M. surname: Drayna fullname: Drayna, Paul M. organization: Department of Ophthalmology and Visual NeurosciencesUniversity of Minnesota – sequence: 4 givenname: Behzad surname: Nazari fullname: Nazari, Behzad organization: Department of Electrical and Computer EngineeringIsfahan University of Technology – sequence: 5 givenname: Saeed surname: Sadri fullname: Sadri, Saeed organization: Department of Electrical and Computer EngineeringIsfahan University of Technology – sequence: 6 givenname: Hossein orcidid: 0000-0002-0551-3636 surname: Rabbani fullname: Rabbani, Hossein organization: Department of Biomedical Engineering, Medical Image and Signal Processing Research CenterIsfahan University of Medical Sciences – sequence: 7 givenname: Keshab K. orcidid: 0000-0001-6543-2793 surname: Parhi fullname: Parhi, Keshab K. email: parhi@umn.edu organization: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28783619$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kcFu2zAQRIkiReOk_YCiQEGgl17kkCIpkkfXtdMASQM0DnoUaGklM5BEhaQO_p1-aWnYzSGHnogl3swuZi7Q2eAGQOgjJXNKib7afLtbzXNC5TyXjBOh3qAZFUJluWD0DM0IoSrTuebn6CKEpzRyxYt36DxXUrGC6hn6s566bo8XU3S9iVDjB2h7GKKJ1g3YNXjdTba-Wu5DxL-gTZ8B2wHfj9FWpsNLtwMPQwV443rXejPu9vimNy0E_NvGHf5uzRYSi-9MNXXG41UNvcGPwQ4t_glT9C64cZeAB4gBm6HG1wcXvOha55NDH96jt43pAnw4vZfocb3aLH9kt_fXN8vFbVYxrmMmCKWsqQvFOZVEM8WYKUhhlKlVzoq6kSAEh0IryVhNc15okLUgedFIwrcVu0Rfj76jd88ThFj2NlTQdWYAN4WS6pQykVqIhH55hT65yQ_pujKnknOpKaOJ-nyipm0PdTl62xu_L_-lnwB6BKqUQvDQvCCUlIeGy0PD5aHh8tRw0shXmsoe24re2O6_yk9HpQWAl01Sa6mZYH8Bu4aytQ |
| CODEN | IEBEAX |
| CitedBy_id | crossref_primary_10_1109_JBHI_2020_2982914 crossref_primary_10_1109_TIP_2022_3148814 crossref_primary_10_1109_JTEHM_2021_3096378 crossref_primary_10_1109_JPHOT_2020_3026973 crossref_primary_10_1371_journal_pone_0186949 crossref_primary_10_1002_ima_22893 crossref_primary_10_1109_ACCESS_2022_3198657 crossref_primary_10_1002_ima_22840 crossref_primary_10_1007_s10916_018_1078_3 crossref_primary_10_1109_TIM_2020_3017037 crossref_primary_10_1109_TMI_2019_2901398 crossref_primary_10_1155_2023_1839387 crossref_primary_10_1109_TETCI_2023_3309626 crossref_primary_10_1109_ACCESS_2020_2983818 crossref_primary_10_1109_TMI_2022_3142048 crossref_primary_10_1007_s11760_023_02959_4 crossref_primary_10_1088_1361_6560_ac7378 crossref_primary_10_1049_sfw2_6006074 crossref_primary_10_1109_JBHI_2018_2810379 crossref_primary_10_1109_TFUZZ_2024_3473310 crossref_primary_10_1109_ACCESS_2020_3017449 |
| Cites_doi | 10.1109/ISBI.2015.7164160 10.2337/dc11-1909 10.1364/BOE.6.000155 10.1016/j.cviu.2011.04.001 10.1049/iet-ipr.2015.0738 10.1109/EMBC.2013.6609778 10.1109/TBME.2012.2184759 10.1167/iovs.12-11396 10.1109/TMI.2012.2191302 10.1364/OE.18.021293 10.1016/j.patcog.2015.02.018 10.1016/j.patcog.2012.09.015 10.1016/j.media.2013.05.006 10.1118/1.4747271 10.1016/j.ins.2016.04.017 10.1109/TMI.2010.2047023 10.1167/iovs.11-7640 10.1109/TMI.2009.2031324 10.1364/BOE.7.001577 10.1109/MIXDES.2015.7208485 10.1016/0898-1221(93)90181-T 10.1136/bjo.80.3.241 10.1016/j.patcog.2008.10.002 10.1016/j.measurement.2014.08.039 10.1016/j.compeleceng.2014.04.020 10.1214/09-SS051 10.4103/2228-7477.186878 10.1016/j.sigpro.2009.10.021 10.1126/science.1957169 10.1109/TMI.2005.848655 10.1109/CBMS.2013.6627825 10.1364/OE.18.019413 10.1364/BOE.6.001172 10.1109/TMI.2008.923966 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 |
| DBID | 97E RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| DOI | 10.1109/TBME.2017.2734058 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Civil Engineering Abstracts Aluminium Industry Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Ceramic Abstracts Materials Business File METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Aerospace Database Engineered Materials Abstracts Biotechnology Research Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE Materials Research Database |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Engineering |
| EISSN | 1558-2531 |
| EndPage | 1001 |
| ExternalDocumentID | 28783619 10_1109_TBME_2017_2734058 7997935 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: Minnesota Lions |
| GroupedDBID | --- -~X .55 .DC .GJ 0R~ 29I 4.4 53G 5GY 5RE 5VS 6IF 6IK 6IL 6IN 85S 97E AAJGR AARMG AASAJ AAWTH AAYJJ ABAZT ABJNI ABQJQ ABVLG ACGFO ACGFS ACIWK ACKIV ACNCT ACPRK ADZIZ AENEX AETIX AFFNX AFRAH AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CHZPO CS3 DU5 EBS EJD F5P HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IEGSK IFIPE IFJZH IPLJI JAVBF LAI MS~ O9- OCL P2P RIA RIE RIL RNS TAE TN5 VH1 VJK X7M ZGI ZXP AAYXX CITATION CGR CUY CVF ECM EIF NPM RIG 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| ID | FETCH-LOGICAL-c349t-50113fd684417093833a606a8ad8236df7e554e698733d12469e7d5026f704bc3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 86 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000430695900005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0018-9294 1558-2531 |
| IngestDate | Sat Sep 27 22:07:32 EDT 2025 Mon Jun 30 08:30:22 EDT 2025 Thu Apr 03 06:56:58 EDT 2025 Sat Nov 29 05:34:21 EST 2025 Tue Nov 18 21:42:11 EST 2025 Wed Aug 27 02:53:44 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c349t-50113fd684417093833a606a8ad8236df7e554e698733d12469e7d5026f704bc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-0551-3636 0000-0001-6543-2793 |
| PMID | 28783619 |
| PQID | 2174479131 |
| PQPubID | 85474 |
| PageCount | 13 |
| ParticipantIDs | proquest_journals_2174479131 crossref_citationtrail_10_1109_TBME_2017_2734058 crossref_primary_10_1109_TBME_2017_2734058 ieee_primary_7997935 proquest_miscellaneous_1927307955 pubmed_primary_28783619 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-05-01 |
| PublicationDateYYYYMMDD | 2018-05-01 |
| PublicationDate_xml | – month: 05 year: 2018 text: 2018-05-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: New York |
| PublicationTitle | IEEE transactions on biomedical engineering |
| PublicationTitleAbbrev | TBME |
| PublicationTitleAlternate | IEEE Trans Biomed Eng |
| PublicationYear | 2018 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 ref37 ref15 ref36 ref14 swingle (ref35) 0; 9417 ref30 bogunovi? (ref27) 0 ref33 ref32 ref10 smarandache (ref7) 2003 rashno (ref39) 2017; 58 debuc (ref6) 0 ref2 ref1 ref17 ref38 ref16 ref19 ref18 esmaeili (ref31) 2016; 6 parhi (ref22) 2017; 58 lee (ref20) 2010; 29 kohler (ref40) 2017; 58 ref24 ref23 ref26 ref25 ref42 ref41 swingle (ref34) 0; 9038 ref21 ref28 ref29 ref8 ref9 ref4 ref3 ref5 heshmati (ref11) 2016; 10 |
| References_xml | – ident: ref36 doi: 10.1109/ISBI.2015.7164160 – year: 2003 ident: ref7 publication-title: A Unifying Field in Logics Neutrosophic Logic Neutrosophy Neutrosophic Set Neutrosophic Probability – ident: ref4 doi: 10.2337/dc11-1909 – ident: ref30 doi: 10.1364/BOE.6.000155 – ident: ref10 doi: 10.1016/j.cviu.2011.04.001 – volume: 10 start-page: 464 year: 2016 ident: ref11 article-title: Scheme for unsupervised colour-texture image segmentation using neutrosophic set and non-subsampled contourlet transform publication-title: IET Image Process doi: 10.1049/iet-ipr.2015.0738 – start-page: 49 year: 0 ident: ref27 article-title: Geodesic graph cut based retinal fluid segmentation in optical coherence tomography publication-title: Ophthalmic Medical Image Analysis – ident: ref29 doi: 10.1109/EMBC.2013.6609778 – ident: ref32 doi: 10.1109/TBME.2012.2184759 – ident: ref28 doi: 10.1167/iovs.12-11396 – ident: ref25 doi: 10.1109/TMI.2012.2191302 – ident: ref18 doi: 10.1364/OE.18.021293 – ident: ref15 doi: 10.1016/j.patcog.2015.02.018 – ident: ref16 doi: 10.1016/j.patcog.2012.09.015 – ident: ref2 doi: 10.1016/j.media.2013.05.006 – ident: ref13 doi: 10.1118/1.4747271 – ident: ref37 doi: 10.1016/j.ins.2016.04.017 – year: 0 ident: ref6 publication-title: A review of algorithms for segmentation of retinal image data using optical coherence tomography – volume: 58 start-page: 4633 year: 2017 ident: ref22 article-title: Automated fluid/cyst segmentation: A quantitative assessment of diabetic macular edema publication-title: Investigative Ophthalmol Vis Sci – ident: ref24 doi: 10.1109/TMI.2010.2047023 – ident: ref21 doi: 10.1167/iovs.11-7640 – volume: 29 start-page: 159 year: 2010 ident: ref20 article-title: Segmentation of the optic disc in 3-d oct scans of the optic nerve head publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2009.2031324 – ident: ref26 doi: 10.1364/BOE.7.001577 – ident: ref33 doi: 10.1109/MIXDES.2015.7208485 – volume: 58 start-page: 397 year: 2017 ident: ref39 article-title: Automated intra-retinal, sub-retinal and sub-rpe cyst regions segmentation in age-related macular degeneration (amd) subjects publication-title: Investigative Ophthalmol Visual Sci – ident: ref41 doi: 10.1016/0898-1221(93)90181-T – ident: ref5 doi: 10.1136/bjo.80.3.241 – ident: ref8 doi: 10.1016/j.patcog.2008.10.002 – volume: 9038 year: 0 ident: ref34 article-title: Microcystic macular edema detection in retina oct images publication-title: Proc SPIE – ident: ref12 doi: 10.1016/j.measurement.2014.08.039 – ident: ref14 doi: 10.1016/j.compeleceng.2014.04.020 – ident: ref42 doi: 10.1214/09-SS051 – volume: 6 start-page: 166 year: 2016 ident: ref31 article-title: Three-dimensional segmentation of retinal cysts from spectral-domain optical coherence tomography images by the use of three-dimensional curvelet based k-svd publication-title: Journal of medical signals and sensors doi: 10.4103/2228-7477.186878 – ident: ref9 doi: 10.1016/j.sigpro.2009.10.021 – ident: ref1 doi: 10.1126/science.1957169 – ident: ref23 doi: 10.1109/TMI.2005.848655 – ident: ref38 doi: 10.1109/CBMS.2013.6627825 – volume: 9417 year: 0 ident: ref35 article-title: Segmentation of microcystic macular edema in cirrus oct scans with an exploratory longitudinal study publication-title: Proc SPIE – ident: ref19 doi: 10.1364/OE.18.019413 – ident: ref3 doi: 10.1364/BOE.6.001172 – volume: 58 start-page: 953 year: 2017 ident: ref40 article-title: Correlation between initial vision and vision improvement with automatically calculated retinal cyst volume in treated dme after resolution publication-title: Investigative Ophthalmol Vis Sci – ident: ref17 doi: 10.1109/TMI.2008.923966 |
| SSID | ssj0014846 |
| Score | 2.5697644 |
| Snippet | This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 989 |
| SubjectTerms | Algorithms Automation Cost function Cysts Cysts - diagnostic imaging Datasets Diabetes Diabetes mellitus Diabetic macular edema Diabetic Retinopathy - diagnostic imaging Edema Epithelium fluid/cyst segmentation graph theory Humans Image Interpretation, Computer-Assisted - methods Image processing Image segmentation Macular Edema - diagnostic imaging neutrosophic set Optical Coherence Tomography Retina Retina - diagnostic imaging Retinal pigment epithelium Sensitivity Sensitivity and Specificity Shortest-path problems Signal processing algorithms Three-dimensional displays Tomography Tomography, Optical Coherence - methods Two dimensional displays |
| Title | Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms |
| URI | https://ieeexplore.ieee.org/document/7997935 https://www.ncbi.nlm.nih.gov/pubmed/28783619 https://www.proquest.com/docview/2174479131 https://www.proquest.com/docview/1927307955 |
| Volume | 65 |
| WOSCitedRecordID | wos000430695900005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-2531 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014846 issn: 0018-9294 databaseCode: RIE dateStart: 19640101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bb9MwFLa2CSF44LJxKYzpIPGEyObGSWw_lqkFJFoQK9C3KLGdUalJpiZB2t_hl3LsuNEeAIm3SLFPIp2Lz-dzI-RVIfHMMXkc5FTIIAp5GGQ6TgIRy4SrIs-lq3r_9pEvFmK1kp_3yJuhFsYY45LPzKl9dLF8XavOXpWdcSlRnOJ9ss8572u1hohBJPqiHDpGBQ5l5COYYyrPlm_nU5vExU9tLxca2xl9CBQES2x_nRvHkZuv8ndX0x05s_v_97MPyD3vWsKkl4WHZM9Uh-TujYaDh-T23IfSj8gvCz6vYdK1NTqtRsOFuSx9IVIFdQGzTYeCen7dtPDF2KzlBtYVfLpyl99gyzpcoSAs69K3vYYPJVqnBr6v2x_Qp9qsFcwzl-oKU23KDFyKAixM127dBAVccGHaBrJKwztLBSaby3qLFMrmEfk6my7P3wd-YEOgWCTbIEZjwQqdCDvXjEoEvyxDgJSJTNu56rrgBr0Xk0jBGdPoWSTScB0jDCw4jXLFHpODqq7MUwJ5nEVoAA1ThYpEHmYszCkSkFQp67aMCN3xLVW-m7kdqrFJHaqhMrVcTy3XU8_1EXk9bLnqW3n8a_GRZemw0HNzRI53wpF6ZW9Si-oiLsdsPCIvh9eopjb2klWm7poUHWm0pVzGSOJJL1QD7Z0sPvvzN5-TO6GVapdleUwO2m1nXpBb6me7brYnqAsrceJ04Teu2gLU |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bb9MwFLbGQFweuGwMCgMOEk-IbE4cx_FjmVo20RbEythb5DjOVqlNpiZB2t_hl3LspNEeAIm3SLFPIp2Lv-NzI-RdLvHMMSn3UhpLLwxE4KmMR17MZSR0nqbSVb2fTcRsFp-fy69b5ENfC2OMccln5sA-ulh-VurGXpUdCilRnPgtcpuHYeC31Vp9zCCM27Ic6qMKBzLsYpg-lYfzj9ORTeMSB7abC-V2Sh-6CjGLbIedGweSm7Dyd7DpDp3xo__73cfkYQcuYdhKwxOyZYod8uBGy8EdcnfaBdN3yS_rfl7DsKlLhK0mg1NzsepKkQoocxgvGxTVo-uqhm_G5i1XsCjgy5W7_gZb2OFKBWFerrrG13CyQvtUwY9FfQltss1Cw1S5ZFcYZWalwCUpwMw09drNUMAFp6auQBUZfLJUYLi8KNdIYVU9Jd_Ho_nRsdeNbPA0C2XtcTQXLM-i2E42oxLdX6bQRVKxyuxk9SwXBvGLiWQsGMsQW0TSiIyjI5gLGqaa7ZHtoizMcwIpVyGaQMN0rsM4DRQLUooEJNXaApcBoRu-JbrrZ27HaiwT59dQmViuJ5brScf1AXnfb7lqm3n8a_GuZWm_sOPmgOxvhCPp1L1KrF8XCukzf0De9q9RUW30RRWmbKoEoTRaUyE5knjWClVPeyOLL_78zTfk3vF8OkkmJ7PPL8n9wEq4y7ncJ9v1ujGvyB39s15U69dOI34DK5kFMw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Fully+Automated+Segmentation+of+Fluid%2FCyst+Regions+in+Optical+Coherence+Tomography+Images+With+Diabetic+Macular+Edema+Using+Neutrosophic+Sets+and+Graph+Algorithms&rft.jtitle=IEEE+transactions+on+biomedical+engineering&rft.au=Rashno%2C+Abdolreza&rft.au=Koozekanani%2C+Dara+D&rft.au=Drayna%2C+Paul+M&rft.au=Nazari%2C+Behzad&rft.date=2018-05-01&rft.issn=1558-2531&rft.eissn=1558-2531&rft.volume=65&rft.issue=5&rft.spage=989&rft_id=info:doi/10.1109%2FTBME.2017.2734058&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9294&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9294&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9294&client=summon |