Automated detection of retinal health using PHOG and SURF features extracted from fundus images View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-05

AUTHORS

Joel E. W. Koh, Eddie Y. K. Ng, Sulatha V. Bhandary, Augustinus Laude, U. Rajendra Acharya

ABSTRACT

Many health-related problems arise with aging. One of the diseases that is prevalent among the elderly is the loss of sight. Various eye diseases, namely age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma are the prime causes of vision loss as we grow old. Nevertheless, early detection of such eye diseases can impede the progression of this problem. Therefore, the elderly are encouraged to attend regular eye checkups for early detection of eye diseases. However, it is time-consuming and laborious to conduct a mass eye screening session frequently. Hence, we proposed a novel approach to develop an automated retinal health screening system in this work. This paper discusses a retinal screening system to automatically differentiate normal image from abnormal (AMD, DR, and glaucoma) fundus images. The fundus images are subjected to the pyramid histogram of oriented gradients (PHOG) and speeded up robust features (SURF) techniques. Then, the extracted data are subjected to adaptive synthetic sampling to balance the number of data in the two classes (normal and abnormal). Subsequently, we employed the canonical correlation analysis approach to fuse the highly-correlated features extracted from the two (PHOG and SURF) descriptors. We have achieved 96.21% accuracy, 95.00% sensitivity, and 97.42% specificity with ten-fold cross-validation strategy using k-nearest neighbor (kNN) classifier. This novel algorithm has high potential in the diagnosis of normal eyes during the mass eye screening session or in polyclinics quickly and reliably. Hence, the patients having abnormal eyes can be sent to the main hospitals which will reduce the workload for the ophthalmologists. Graphical AbstractProposed system Proposed system More... »

PAGES

1379-1393

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10489-017-1048-3

DOI

http://dx.doi.org/10.1007/s10489-017-1048-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091373591


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1113", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Ophthalmology and Optometry", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Nanyang Technological University", 
          "id": "https://www.grid.ac/institutes/grid.59025.3b", 
          "name": [
            "Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore, Singapore", 
            "School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore, Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Koh", 
        "givenName": "Joel E. W.", 
        "id": "sg:person.01124741110.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01124741110.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Nanyang Technological University", 
          "id": "https://www.grid.ac/institutes/grid.59025.3b", 
          "name": [
            "School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore, Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ng", 
        "givenName": "Eddie Y. K.", 
        "id": "sg:person.07426566722.21", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07426566722.21"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Kasturba Medical College", 
          "id": "https://www.grid.ac/institutes/grid.465547.1", 
          "name": [
            "Department of Ophthalmology, Kasturba Medical College, 576104, Manipal, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bhandary", 
        "givenName": "Sulatha V.", 
        "id": "sg:person.01134232635.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134232635.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tan Tock Seng Hospital", 
          "id": "https://www.grid.ac/institutes/grid.240988.f", 
          "name": [
            "National Healthcare Group Eye Institute, Tan Tock Seng Hospital, 308433, Singapore, Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Laude", 
        "givenName": "Augustinus", 
        "id": "sg:person.0625131734.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0625131734.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Malaya", 
          "id": "https://www.grid.ac/institutes/grid.10347.31", 
          "name": [
            "Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore, Singapore", 
            "Department of Biomedical Engineering, School of Science and Technology, SUSS University, 599491, Singapore, Singapore", 
            "Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Acharya", 
        "givenName": "U. Rajendra", 
        "id": "sg:person.0734110256.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734110256.51"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s0377-2217(02)00911-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000149729"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0377-2217(02)00911-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000149729"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1472-6947-14-80", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000864626", 
          "https://doi.org/10.1186/1472-6947-14-80"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.media.2009.12.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002089882"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1167/iovs.10-7075", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005778711"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiomed.2014.07.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007692016"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-007-9113-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008604116", 
          "https://doi.org/10.1007/s10916-007-9113-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bspc.2014.09.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011383354"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.knosys.2012.09.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012221187"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ins.2007.07.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012322640"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-008-9154-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014420073", 
          "https://doi.org/10.1007/s10916-008-9154-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.knosys.2012.02.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014768477"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiomed.2008.02.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016598117"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiomed.2016.04.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016922042"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11517-014-1167-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017190317", 
          "https://doi.org/10.1007/s11517-014-1167-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0734-189x(87)80186-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019291816"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/b:vlsi.0000028532.53893.82", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020104799", 
          "https://doi.org/10.1023/b:vlsi.0000028532.53893.82"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-010-9454-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020222262", 
          "https://doi.org/10.1007/s10916-010-9454-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bjo.80.11.940", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022632883"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0161-6420(13)38012-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023274010"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1464-5491.2003.01085.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025538580"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.knosys.2015.09.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030568750"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1167/iovs.12-9576", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031838459"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1282280.1282340", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033635059"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiomed.2016.04.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034850786"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ophtha.2010.03.046", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037010465"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.knosys.2011.07.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037338477"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiomed.2015.05.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038623288"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bspc.2013.11.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038681917"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cviu.2007.09.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040969278"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-011-9663-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041986430", 
          "https://doi.org/10.1007/s10916-011-9663-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-008-9210-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046774020", 
          "https://doi.org/10.1007/s10916-008-9210-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11517-014-1180-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047300719", 
          "https://doi.org/10.1007/s11517-014-1180-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.knosys.2011.06.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050760179"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patcog.2004.12.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051913679"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-008-9195-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053480664", 
          "https://doi.org/10.1007/s10916-008-9195-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/tmj.2005.11.668", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059322643"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jbhi.2016.2544961", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061277245"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2012.2193126", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061528778"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.1967.1053964", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061646286"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/titb.2011.2119322", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061657001"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/titb.2011.2176540", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061657084"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2009.2033909", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061695478"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2009.2037146", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061695498"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0954411912470240", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063888676"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0954411912470240", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063888676"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1243/09544119jeim486", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064455088"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1243/09544119jeim486", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064455088"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiomed.2017.03.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084067312"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiomed.2017.03.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084067320"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jocs.2017.02.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084090483"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jocs.2017.03.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084090494"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiomed.2017.06.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086096028"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3109/9781420020977-19", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088311503"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiomed.2017.06.022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090382843"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icnn.1995.488968", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093669333"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ijcnn.2008.4633969", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094491390"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-05", 
    "datePublishedReg": "2018-05-01", 
    "description": "Many health-related problems arise with aging. One of the diseases that is prevalent among the elderly is the loss of sight. Various eye diseases, namely age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma are the prime causes of vision loss as we grow old. Nevertheless, early detection of such eye diseases can impede the progression of this problem. Therefore, the elderly are encouraged to attend regular eye checkups for early detection of eye diseases. However, it is time-consuming and laborious to conduct a mass eye screening session frequently. Hence, we proposed a novel approach to develop an automated retinal health screening system in this work. This paper discusses a retinal screening system to automatically differentiate normal image from abnormal (AMD, DR, and glaucoma) fundus images. The fundus images are subjected to the pyramid histogram of oriented gradients (PHOG) and speeded up robust features (SURF) techniques. Then, the extracted data are subjected to adaptive synthetic sampling to balance the number of data in the two classes (normal and abnormal). Subsequently, we employed the canonical correlation analysis approach to fuse the highly-correlated features extracted from the two (PHOG and SURF) descriptors. We have achieved 96.21% accuracy, 95.00% sensitivity, and 97.42% specificity with ten-fold cross-validation strategy using k-nearest neighbor (kNN) classifier. This novel algorithm has high potential in the diagnosis of normal eyes during the mass eye screening session or in polyclinics quickly and reliably. Hence, the patients having abnormal eyes can be sent to the main hospitals which will reduce the workload for the ophthalmologists. Graphical AbstractProposed system Proposed system", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10489-017-1048-3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136076", 
        "issn": [
          "0924-669X", 
          "1573-7497"
        ], 
        "name": "Applied Intelligence", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "48"
      }
    ], 
    "name": "Automated detection of retinal health using PHOG and SURF features extracted from fundus images", 
    "pagination": "1379-1393", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "93af285642854263f621cf865afec5ef5eb1052ec67dcfc99621889c24d8e960"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10489-017-1048-3"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1091373591"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10489-017-1048-3", 
      "https://app.dimensions.ai/details/publication/pub.1091373591"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T16:55", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8669_00000601.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s10489-017-1048-3"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10489-017-1048-3'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10489-017-1048-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10489-017-1048-3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10489-017-1048-3'


 

This table displays all metadata directly associated to this object as RDF triples.

273 TRIPLES      21 PREDICATES      81 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10489-017-1048-3 schema:about anzsrc-for:11
2 anzsrc-for:1113
3 schema:author N04422efc9e2242cfaa7fa92a3f580c81
4 schema:citation sg:pub.10.1007/s10916-007-9113-9
5 sg:pub.10.1007/s10916-008-9154-8
6 sg:pub.10.1007/s10916-008-9195-z
7 sg:pub.10.1007/s10916-008-9210-4
8 sg:pub.10.1007/s10916-010-9454-7
9 sg:pub.10.1007/s10916-011-9663-8
10 sg:pub.10.1007/s11517-014-1167-5
11 sg:pub.10.1007/s11517-014-1180-8
12 sg:pub.10.1023/b:vlsi.0000028532.53893.82
13 sg:pub.10.1186/1472-6947-14-80
14 https://doi.org/10.1016/j.bspc.2013.11.006
15 https://doi.org/10.1016/j.bspc.2014.09.004
16 https://doi.org/10.1016/j.compbiomed.2008.02.008
17 https://doi.org/10.1016/j.compbiomed.2014.07.015
18 https://doi.org/10.1016/j.compbiomed.2015.05.019
19 https://doi.org/10.1016/j.compbiomed.2016.04.009
20 https://doi.org/10.1016/j.compbiomed.2016.04.015
21 https://doi.org/10.1016/j.compbiomed.2017.03.008
22 https://doi.org/10.1016/j.compbiomed.2017.03.016
23 https://doi.org/10.1016/j.compbiomed.2017.06.017
24 https://doi.org/10.1016/j.compbiomed.2017.06.022
25 https://doi.org/10.1016/j.cviu.2007.09.014
26 https://doi.org/10.1016/j.ins.2007.07.020
27 https://doi.org/10.1016/j.jocs.2017.02.006
28 https://doi.org/10.1016/j.jocs.2017.03.005
29 https://doi.org/10.1016/j.knosys.2011.06.013
30 https://doi.org/10.1016/j.knosys.2011.07.002
31 https://doi.org/10.1016/j.knosys.2012.02.010
32 https://doi.org/10.1016/j.knosys.2012.09.008
33 https://doi.org/10.1016/j.knosys.2015.09.012
34 https://doi.org/10.1016/j.media.2009.12.006
35 https://doi.org/10.1016/j.ophtha.2010.03.046
36 https://doi.org/10.1016/j.patcog.2004.12.013
37 https://doi.org/10.1016/s0161-6420(13)38012-9
38 https://doi.org/10.1016/s0377-2217(02)00911-6
39 https://doi.org/10.1016/s0734-189x(87)80186-x
40 https://doi.org/10.1046/j.1464-5491.2003.01085.x
41 https://doi.org/10.1089/tmj.2005.11.668
42 https://doi.org/10.1109/icnn.1995.488968
43 https://doi.org/10.1109/ijcnn.2008.4633969
44 https://doi.org/10.1109/jbhi.2016.2544961
45 https://doi.org/10.1109/tbme.2012.2193126
46 https://doi.org/10.1109/tit.1967.1053964
47 https://doi.org/10.1109/titb.2011.2119322
48 https://doi.org/10.1109/titb.2011.2176540
49 https://doi.org/10.1109/tmi.2009.2033909
50 https://doi.org/10.1109/tmi.2009.2037146
51 https://doi.org/10.1136/bjo.80.11.940
52 https://doi.org/10.1145/1282280.1282340
53 https://doi.org/10.1167/iovs.10-7075
54 https://doi.org/10.1167/iovs.12-9576
55 https://doi.org/10.1177/0954411912470240
56 https://doi.org/10.1243/09544119jeim486
57 https://doi.org/10.3109/9781420020977-19
58 schema:datePublished 2018-05
59 schema:datePublishedReg 2018-05-01
60 schema:description Many health-related problems arise with aging. One of the diseases that is prevalent among the elderly is the loss of sight. Various eye diseases, namely age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma are the prime causes of vision loss as we grow old. Nevertheless, early detection of such eye diseases can impede the progression of this problem. Therefore, the elderly are encouraged to attend regular eye checkups for early detection of eye diseases. However, it is time-consuming and laborious to conduct a mass eye screening session frequently. Hence, we proposed a novel approach to develop an automated retinal health screening system in this work. This paper discusses a retinal screening system to automatically differentiate normal image from abnormal (AMD, DR, and glaucoma) fundus images. The fundus images are subjected to the pyramid histogram of oriented gradients (PHOG) and speeded up robust features (SURF) techniques. Then, the extracted data are subjected to adaptive synthetic sampling to balance the number of data in the two classes (normal and abnormal). Subsequently, we employed the canonical correlation analysis approach to fuse the highly-correlated features extracted from the two (PHOG and SURF) descriptors. We have achieved 96.21% accuracy, 95.00% sensitivity, and 97.42% specificity with ten-fold cross-validation strategy using k-nearest neighbor (kNN) classifier. This novel algorithm has high potential in the diagnosis of normal eyes during the mass eye screening session or in polyclinics quickly and reliably. Hence, the patients having abnormal eyes can be sent to the main hospitals which will reduce the workload for the ophthalmologists. Graphical AbstractProposed system Proposed system
61 schema:genre research_article
62 schema:inLanguage en
63 schema:isAccessibleForFree false
64 schema:isPartOf N23481b9981b84ed6a55a8e47ff7a5780
65 Nd3a891767b564f42a56359dd33eeb7e2
66 sg:journal.1136076
67 schema:name Automated detection of retinal health using PHOG and SURF features extracted from fundus images
68 schema:pagination 1379-1393
69 schema:productId N3aad2c9a0f174895b278a3c4e0e07c7a
70 N5caeac1747e9474e849f0442ce8750a7
71 Ndbb9ded0d32a4cc680835cc6061e147b
72 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091373591
73 https://doi.org/10.1007/s10489-017-1048-3
74 schema:sdDatePublished 2019-04-10T16:55
75 schema:sdLicense https://scigraph.springernature.com/explorer/license/
76 schema:sdPublisher Nc251c3d4ebb64162ab617830d1d5cb6b
77 schema:url http://link.springer.com/10.1007/s10489-017-1048-3
78 sgo:license sg:explorer/license/
79 sgo:sdDataset articles
80 rdf:type schema:ScholarlyArticle
81 N04422efc9e2242cfaa7fa92a3f580c81 rdf:first sg:person.01124741110.05
82 rdf:rest N7aed2c23e64b425787a0b5d6fc434a4d
83 N23481b9981b84ed6a55a8e47ff7a5780 schema:volumeNumber 48
84 rdf:type schema:PublicationVolume
85 N2c81600877de497688b1abff80ed262f rdf:first sg:person.0734110256.51
86 rdf:rest rdf:nil
87 N3aad2c9a0f174895b278a3c4e0e07c7a schema:name dimensions_id
88 schema:value pub.1091373591
89 rdf:type schema:PropertyValue
90 N55738f696a8347d9a7ecc41ad24ef907 rdf:first sg:person.0625131734.79
91 rdf:rest N2c81600877de497688b1abff80ed262f
92 N5caeac1747e9474e849f0442ce8750a7 schema:name doi
93 schema:value 10.1007/s10489-017-1048-3
94 rdf:type schema:PropertyValue
95 N7aed2c23e64b425787a0b5d6fc434a4d rdf:first sg:person.07426566722.21
96 rdf:rest Nca7e6253edad4684a972a6c97aa8754b
97 Nc251c3d4ebb64162ab617830d1d5cb6b schema:name Springer Nature - SN SciGraph project
98 rdf:type schema:Organization
99 Nca7e6253edad4684a972a6c97aa8754b rdf:first sg:person.01134232635.15
100 rdf:rest N55738f696a8347d9a7ecc41ad24ef907
101 Nd3a891767b564f42a56359dd33eeb7e2 schema:issueNumber 5
102 rdf:type schema:PublicationIssue
103 Ndbb9ded0d32a4cc680835cc6061e147b schema:name readcube_id
104 schema:value 93af285642854263f621cf865afec5ef5eb1052ec67dcfc99621889c24d8e960
105 rdf:type schema:PropertyValue
106 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
107 schema:name Medical and Health Sciences
108 rdf:type schema:DefinedTerm
109 anzsrc-for:1113 schema:inDefinedTermSet anzsrc-for:
110 schema:name Ophthalmology and Optometry
111 rdf:type schema:DefinedTerm
112 sg:journal.1136076 schema:issn 0924-669X
113 1573-7497
114 schema:name Applied Intelligence
115 rdf:type schema:Periodical
116 sg:person.01124741110.05 schema:affiliation https://www.grid.ac/institutes/grid.59025.3b
117 schema:familyName Koh
118 schema:givenName Joel E. W.
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01124741110.05
120 rdf:type schema:Person
121 sg:person.01134232635.15 schema:affiliation https://www.grid.ac/institutes/grid.465547.1
122 schema:familyName Bhandary
123 schema:givenName Sulatha V.
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134232635.15
125 rdf:type schema:Person
126 sg:person.0625131734.79 schema:affiliation https://www.grid.ac/institutes/grid.240988.f
127 schema:familyName Laude
128 schema:givenName Augustinus
129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0625131734.79
130 rdf:type schema:Person
131 sg:person.0734110256.51 schema:affiliation https://www.grid.ac/institutes/grid.10347.31
132 schema:familyName Acharya
133 schema:givenName U. Rajendra
134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734110256.51
135 rdf:type schema:Person
136 sg:person.07426566722.21 schema:affiliation https://www.grid.ac/institutes/grid.59025.3b
137 schema:familyName Ng
138 schema:givenName Eddie Y. K.
139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07426566722.21
140 rdf:type schema:Person
141 sg:pub.10.1007/s10916-007-9113-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008604116
142 https://doi.org/10.1007/s10916-007-9113-9
143 rdf:type schema:CreativeWork
144 sg:pub.10.1007/s10916-008-9154-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014420073
145 https://doi.org/10.1007/s10916-008-9154-8
146 rdf:type schema:CreativeWork
147 sg:pub.10.1007/s10916-008-9195-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1053480664
148 https://doi.org/10.1007/s10916-008-9195-z
149 rdf:type schema:CreativeWork
150 sg:pub.10.1007/s10916-008-9210-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046774020
151 https://doi.org/10.1007/s10916-008-9210-4
152 rdf:type schema:CreativeWork
153 sg:pub.10.1007/s10916-010-9454-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020222262
154 https://doi.org/10.1007/s10916-010-9454-7
155 rdf:type schema:CreativeWork
156 sg:pub.10.1007/s10916-011-9663-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041986430
157 https://doi.org/10.1007/s10916-011-9663-8
158 rdf:type schema:CreativeWork
159 sg:pub.10.1007/s11517-014-1167-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017190317
160 https://doi.org/10.1007/s11517-014-1167-5
161 rdf:type schema:CreativeWork
162 sg:pub.10.1007/s11517-014-1180-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047300719
163 https://doi.org/10.1007/s11517-014-1180-8
164 rdf:type schema:CreativeWork
165 sg:pub.10.1023/b:vlsi.0000028532.53893.82 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020104799
166 https://doi.org/10.1023/b:vlsi.0000028532.53893.82
167 rdf:type schema:CreativeWork
168 sg:pub.10.1186/1472-6947-14-80 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000864626
169 https://doi.org/10.1186/1472-6947-14-80
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/j.bspc.2013.11.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038681917
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/j.bspc.2014.09.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011383354
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/j.compbiomed.2008.02.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016598117
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/j.compbiomed.2014.07.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007692016
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1016/j.compbiomed.2015.05.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038623288
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1016/j.compbiomed.2016.04.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034850786
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1016/j.compbiomed.2016.04.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016922042
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1016/j.compbiomed.2017.03.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084067312
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1016/j.compbiomed.2017.03.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084067320
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1016/j.compbiomed.2017.06.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086096028
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1016/j.compbiomed.2017.06.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090382843
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1016/j.cviu.2007.09.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040969278
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1016/j.ins.2007.07.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012322640
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1016/j.jocs.2017.02.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084090483
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1016/j.jocs.2017.03.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084090494
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1016/j.knosys.2011.06.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050760179
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1016/j.knosys.2011.07.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037338477
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1016/j.knosys.2012.02.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014768477
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1016/j.knosys.2012.09.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012221187
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1016/j.knosys.2015.09.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030568750
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1016/j.media.2009.12.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002089882
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1016/j.ophtha.2010.03.046 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037010465
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1016/j.patcog.2004.12.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051913679
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1016/s0161-6420(13)38012-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023274010
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1016/s0377-2217(02)00911-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000149729
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1016/s0734-189x(87)80186-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1019291816
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1046/j.1464-5491.2003.01085.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1025538580
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1089/tmj.2005.11.668 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059322643
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1109/icnn.1995.488968 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093669333
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1109/ijcnn.2008.4633969 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094491390
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1109/jbhi.2016.2544961 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061277245
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1109/tbme.2012.2193126 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061528778
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1109/tit.1967.1053964 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061646286
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1109/titb.2011.2119322 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061657001
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1109/titb.2011.2176540 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061657084
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1109/tmi.2009.2033909 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061695478
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1109/tmi.2009.2037146 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061695498
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1136/bjo.80.11.940 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022632883
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1145/1282280.1282340 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033635059
248 rdf:type schema:CreativeWork
249 https://doi.org/10.1167/iovs.10-7075 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005778711
250 rdf:type schema:CreativeWork
251 https://doi.org/10.1167/iovs.12-9576 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031838459
252 rdf:type schema:CreativeWork
253 https://doi.org/10.1177/0954411912470240 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063888676
254 rdf:type schema:CreativeWork
255 https://doi.org/10.1243/09544119jeim486 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064455088
256 rdf:type schema:CreativeWork
257 https://doi.org/10.3109/9781420020977-19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088311503
258 rdf:type schema:CreativeWork
259 https://www.grid.ac/institutes/grid.10347.31 schema:alternateName University of Malaya
260 schema:name Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
261 Department of Biomedical Engineering, School of Science and Technology, SUSS University, 599491, Singapore, Singapore
262 Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore, Singapore
263 rdf:type schema:Organization
264 https://www.grid.ac/institutes/grid.240988.f schema:alternateName Tan Tock Seng Hospital
265 schema:name National Healthcare Group Eye Institute, Tan Tock Seng Hospital, 308433, Singapore, Singapore
266 rdf:type schema:Organization
267 https://www.grid.ac/institutes/grid.465547.1 schema:alternateName Kasturba Medical College
268 schema:name Department of Ophthalmology, Kasturba Medical College, 576104, Manipal, India
269 rdf:type schema:Organization
270 https://www.grid.ac/institutes/grid.59025.3b schema:alternateName Nanyang Technological University
271 schema:name Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore, Singapore
272 School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore, Singapore
273 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...