A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma
Glaucoma is one of the leading severe retinal disease which damages the optic nerve head on the retinal part of the eye irreversibly. Once the person is diagnosed with glaucoma, it cannot be treated entirely, but it can be controlled. If glaucoma is not diagnosed in time, it will lead to vision loss...
Gespeichert in:
| Veröffentlicht in: | SN applied sciences 2020-09, Vol.2 (9), p.1476, Article 1476 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 9 |
| container_start_page | 1476 |
| container_title | SN applied sciences |
| container_volume | 2 |
| creator | Veena, H N Muruganandham, A Kumaran, T Senthil |
| description | Glaucoma is one of the leading severe retinal disease which damages the optic nerve head on the retinal part of the eye irreversibly. Once the person is diagnosed with glaucoma, it cannot be treated entirely, but it can be controlled. If glaucoma is not diagnosed in time, it will lead to vision loss by damaging the Optic Nerve Head. The glaucoma detection is performed based on the optic disc and optic cup parameters on the retinal part of the eye. In the existing system, many image processing and machine learning techniques used for the segmentation and classification of optic disc and optic cup. To improve the precision of diagnosis, the existing techniques used need an improvement. This article helps readers with more information about the existing methods applied for the diagnosis of glaucoma, it also lists the research gaps and technical challenges to improve the accuracy of segmentation and classification methods. |
| doi_str_mv | 10.1007/s42452-020-03221-z |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2788446146</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2788446146</sourcerecordid><originalsourceid>FETCH-LOGICAL-c363t-f73e5ac19b20f828cea3a520b0c81b918b5827b57817577c0884737f7bcfbdce3</originalsourceid><addsrcrecordid>eNp9kF1LwzAUhoMoOOb-gFcBr6v5aJvscgy_YCCIXoc0Pek6uqYm7cT9An-22Tr0zqvz9byHc16Erim5pYSIu5CyNGMJYSQhnDGa7M_QhGWMJ3wu6PlvnvNLNAthQwhhYs5TySfoe4FfYVfDJ3Yt7teAXdfXBpd1MFi35ak0Q4cDVFtoe93XkTyMTKNDqG1tTq2u806bNQTsduCxh75udYPt0JZDwPVWV3Fknccl9GCOGmdx1ejBuK2-QhdWNwFmpzhF7w_3b8unZPXy-LxcrBLDc94nVnDItKHzghErmTSguc4YKYiRtJhTWWSSiSITkopMCEOkTAUXVhTGFqUBPkU349547ccAoVcbN_h4aFBMRDjNaZpHio2U8S4ED1Z1Pn7gvxQl6mC6Gk1X0XR1NF3to4iPohDhtgL_t_of1Q9waodC</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2788446146</pqid></control><display><type>article</type><title>A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>Veena, H N ; Muruganandham, A ; Kumaran, T Senthil</creator><creatorcontrib>Veena, H N ; Muruganandham, A ; Kumaran, T Senthil</creatorcontrib><description>Glaucoma is one of the leading severe retinal disease which damages the optic nerve head on the retinal part of the eye irreversibly. Once the person is diagnosed with glaucoma, it cannot be treated entirely, but it can be controlled. If glaucoma is not diagnosed in time, it will lead to vision loss by damaging the Optic Nerve Head. The glaucoma detection is performed based on the optic disc and optic cup parameters on the retinal part of the eye. In the existing system, many image processing and machine learning techniques used for the segmentation and classification of optic disc and optic cup. To improve the precision of diagnosis, the existing techniques used need an improvement. This article helps readers with more information about the existing methods applied for the diagnosis of glaucoma, it also lists the research gaps and technical challenges to improve the accuracy of segmentation and classification methods.</description><identifier>ISSN: 2523-3963</identifier><identifier>EISSN: 2523-3971</identifier><identifier>DOI: 10.1007/s42452-020-03221-z</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Alliances ; Applied and Technical Physics ; Business metrics ; Chemistry/Food Science ; Classification ; Cornea ; Deep learning ; Diabetic retinopathy ; Diagnosis ; Earth Sciences ; Engineering ; Engineering: Digital Image Processing ; Environment ; Eye ; Eye (anatomy) ; Eye diseases ; Glaucoma ; Image processing ; Image segmentation ; Iris ; Machine learning ; Materials Science ; Measurement techniques ; Medical imaging ; Medical research ; Nerves ; Ophthalmology ; Optic nerve ; Retina ; Review Paper</subject><ispartof>SN applied sciences, 2020-09, Vol.2 (9), p.1476, Article 1476</ispartof><rights>Springer Nature Switzerland AG 2020</rights><rights>Springer Nature Switzerland AG 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-f73e5ac19b20f828cea3a520b0c81b918b5827b57817577c0884737f7bcfbdce3</citedby><cites>FETCH-LOGICAL-c363t-f73e5ac19b20f828cea3a520b0c81b918b5827b57817577c0884737f7bcfbdce3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Veena, H N</creatorcontrib><creatorcontrib>Muruganandham, A</creatorcontrib><creatorcontrib>Kumaran, T Senthil</creatorcontrib><title>A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma</title><title>SN applied sciences</title><addtitle>SN Appl. Sci</addtitle><description>Glaucoma is one of the leading severe retinal disease which damages the optic nerve head on the retinal part of the eye irreversibly. Once the person is diagnosed with glaucoma, it cannot be treated entirely, but it can be controlled. If glaucoma is not diagnosed in time, it will lead to vision loss by damaging the Optic Nerve Head. The glaucoma detection is performed based on the optic disc and optic cup parameters on the retinal part of the eye. In the existing system, many image processing and machine learning techniques used for the segmentation and classification of optic disc and optic cup. To improve the precision of diagnosis, the existing techniques used need an improvement. This article helps readers with more information about the existing methods applied for the diagnosis of glaucoma, it also lists the research gaps and technical challenges to improve the accuracy of segmentation and classification methods.</description><subject>Alliances</subject><subject>Applied and Technical Physics</subject><subject>Business metrics</subject><subject>Chemistry/Food Science</subject><subject>Classification</subject><subject>Cornea</subject><subject>Deep learning</subject><subject>Diabetic retinopathy</subject><subject>Diagnosis</subject><subject>Earth Sciences</subject><subject>Engineering</subject><subject>Engineering: Digital Image Processing</subject><subject>Environment</subject><subject>Eye</subject><subject>Eye (anatomy)</subject><subject>Eye diseases</subject><subject>Glaucoma</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Iris</subject><subject>Machine learning</subject><subject>Materials Science</subject><subject>Measurement techniques</subject><subject>Medical imaging</subject><subject>Medical research</subject><subject>Nerves</subject><subject>Ophthalmology</subject><subject>Optic nerve</subject><subject>Retina</subject><subject>Review Paper</subject><issn>2523-3963</issn><issn>2523-3971</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kF1LwzAUhoMoOOb-gFcBr6v5aJvscgy_YCCIXoc0Pek6uqYm7cT9An-22Tr0zqvz9byHc16Erim5pYSIu5CyNGMJYSQhnDGa7M_QhGWMJ3wu6PlvnvNLNAthQwhhYs5TySfoe4FfYVfDJ3Yt7teAXdfXBpd1MFi35ak0Q4cDVFtoe93XkTyMTKNDqG1tTq2u806bNQTsduCxh75udYPt0JZDwPVWV3Fknccl9GCOGmdx1ejBuK2-QhdWNwFmpzhF7w_3b8unZPXy-LxcrBLDc94nVnDItKHzghErmTSguc4YKYiRtJhTWWSSiSITkopMCEOkTAUXVhTGFqUBPkU349547ccAoVcbN_h4aFBMRDjNaZpHio2U8S4ED1Z1Pn7gvxQl6mC6Gk1X0XR1NF3to4iPohDhtgL_t_of1Q9waodC</recordid><startdate>20200901</startdate><enddate>20200901</enddate><creator>Veena, H N</creator><creator>Muruganandham, A</creator><creator>Kumaran, T Senthil</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20200901</creationdate><title>A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma</title><author>Veena, H N ; Muruganandham, A ; Kumaran, T Senthil</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-f73e5ac19b20f828cea3a520b0c81b918b5827b57817577c0884737f7bcfbdce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Alliances</topic><topic>Applied and Technical Physics</topic><topic>Business metrics</topic><topic>Chemistry/Food Science</topic><topic>Classification</topic><topic>Cornea</topic><topic>Deep learning</topic><topic>Diabetic retinopathy</topic><topic>Diagnosis</topic><topic>Earth Sciences</topic><topic>Engineering</topic><topic>Engineering: Digital Image Processing</topic><topic>Environment</topic><topic>Eye</topic><topic>Eye (anatomy)</topic><topic>Eye diseases</topic><topic>Glaucoma</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Iris</topic><topic>Machine learning</topic><topic>Materials Science</topic><topic>Measurement techniques</topic><topic>Medical imaging</topic><topic>Medical research</topic><topic>Nerves</topic><topic>Ophthalmology</topic><topic>Optic nerve</topic><topic>Retina</topic><topic>Review Paper</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Veena, H N</creatorcontrib><creatorcontrib>Muruganandham, A</creatorcontrib><creatorcontrib>Kumaran, T Senthil</creatorcontrib><collection>CrossRef</collection><jtitle>SN applied sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Veena, H N</au><au>Muruganandham, A</au><au>Kumaran, T Senthil</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma</atitle><jtitle>SN applied sciences</jtitle><stitle>SN Appl. Sci</stitle><date>2020-09-01</date><risdate>2020</risdate><volume>2</volume><issue>9</issue><spage>1476</spage><pages>1476-</pages><artnum>1476</artnum><issn>2523-3963</issn><eissn>2523-3971</eissn><abstract>Glaucoma is one of the leading severe retinal disease which damages the optic nerve head on the retinal part of the eye irreversibly. Once the person is diagnosed with glaucoma, it cannot be treated entirely, but it can be controlled. If glaucoma is not diagnosed in time, it will lead to vision loss by damaging the Optic Nerve Head. The glaucoma detection is performed based on the optic disc and optic cup parameters on the retinal part of the eye. In the existing system, many image processing and machine learning techniques used for the segmentation and classification of optic disc and optic cup. To improve the precision of diagnosis, the existing techniques used need an improvement. This article helps readers with more information about the existing methods applied for the diagnosis of glaucoma, it also lists the research gaps and technical challenges to improve the accuracy of segmentation and classification methods.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s42452-020-03221-z</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2523-3963 |
| ispartof | SN applied sciences, 2020-09, Vol.2 (9), p.1476, Article 1476 |
| issn | 2523-3963 2523-3971 |
| language | eng |
| recordid | cdi_proquest_journals_2788446146 |
| source | EZB-FREE-00999 freely available EZB journals |
| subjects | Alliances Applied and Technical Physics Business metrics Chemistry/Food Science Classification Cornea Deep learning Diabetic retinopathy Diagnosis Earth Sciences Engineering Engineering: Digital Image Processing Environment Eye Eye (anatomy) Eye diseases Glaucoma Image processing Image segmentation Iris Machine learning Materials Science Measurement techniques Medical imaging Medical research Nerves Ophthalmology Optic nerve Retina Review Paper |
| title | A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T01%3A26%3A06IST&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=A%20Review%20on%20the%20optic%20disc%20and%20optic%20cup%20segmentation%20and%20classification%20approaches%20over%20retinal%20fundus%20images%20for%20detection%20of%20glaucoma&rft.jtitle=SN%20applied%20sciences&rft.au=Veena,%20H%20N&rft.date=2020-09-01&rft.volume=2&rft.issue=9&rft.spage=1476&rft.pages=1476-&rft.artnum=1476&rft.issn=2523-3963&rft.eissn=2523-3971&rft_id=info:doi/10.1007/s42452-020-03221-z&rft_dat=%3Cproquest_cross%3E2788446146%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=2788446146&rft_id=info:pmid/&rfr_iscdi=true |