H-OCS: A hybrid optic cup segmentation of retinal images

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dc.authorid0000-0001-6657-9738
dc.contributor.authorSarhan, Abdullah
dc.contributor.authorRokne, Jone
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2021-12-03T06:09:51Z
dc.date.available2021-12-03T06:09:51Z
dc.date.issued2021
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractGlaucoma is the second leading cause of irreversible vision loss. Early diagnosis and treatment can, however, slow the progression of the disease. Specialists making this diagnosis rely on several tests and examinations such as visual field tests and examinations of retinal images and optical coherence tomography images. One of the regions examined by specialists when checking for retinal conditions is the optic nerve head region, which is the brightest region in retinal images. Within this region, the ratio between the cup and the disc can be used when diagnosing for glaucoma. Calculating the cup–disc ratio requires the segmentation of both the disc and the cup from retinal images. In a previous paper, a method for segmenting the disc was proposed. Here another deep learning model, H-OCS, is proposed for segmenting the cup from retinal images. A customized InceptionV3 model with transfer learning and image augmentation is used. Additionally, the output of H-OCS is refined and enhanced using a series of post-processing steps. H-OCS is tested on six publicly available datasets: RimOneV3, Drishti, Messidor, Refuge, Riga, and Magrebia and several ablation studies are conducted to evaluate the effectiveness of the proposed approach. Additionally, the performance of H-OCS is compare with other studies. An overall average accuracy of 97.86%, DC of 88.37%, Sensitivity of 89.09% and IoU of 79.66% was achieved.
dc.identifier.citationSarhan, A., Rokne, J. ve Alhajj, R. (2021). H-OCS: A hybrid optic cup segmentation of retinal images. 19th International Conference on Computer Analysis of Images and Patterns, CAIP. Virtual, Online, 28-30 September 2021. https://dx.doi.org/10.1007/978-3-030-89128-2_12
dc.identifier.doi10.1007/978-3-030-89128-2_12
dc.identifier.isbn9783030891275
dc.identifier.issn0302-9743
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://dx.doi.org/10.1007/978-3-030-89128-2_12
dc.identifier.urihttps://hdl.handle.net/20.500.12511/8603
dc.identifier.volume13052
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartof19th International Conference on Computer Analysis of Images and Patterns, CAIPen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCup
dc.subjectDeep Learning
dc.subjectInception
dc.subjectLoss Function
dc.subjectRetinal Image
dc.subjectSegmentation
dc.subjectTransfer Learning
dc.titleH-OCS: A hybrid optic cup segmentation of retinal images
dc.typeConference Object

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