Local information pattern descriptor for corneal diseases diagnosis

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dc.contributor.authorJameel, Samer Kais
dc.contributor.authorAydin, Sezgin
dc.contributor.authorGhaeb, Nebras H.
dc.date.accessioned2025-03-17T12:22:52Z
dc.date.available2025-03-17T12:22:52Z
dc.date.issued2021
dc.departmentTarsus Üniversitesi
dc.description.abstractLight penetrates the human eye through the cornea, which is the outer part of the eye, and then the cornea directs it to the pupil to determine the amount of light that reaches the lens of the eye. Accordingly, the human cornea must not be exposed to any damage or disease that may lead to human vision disturbances. Such damages can be revealed by topographic images used by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms, particularly, use of local feature extractions for the image. Accordingly, we suggest a new algorithm called local information pattern (LIP) descriptor to overcome the lack of local binary patterns that loss of information from the image and solve the problem of image rotation. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast based centre (CBC). On the other hand, calculating local pattern (LP) for each block image, to distinguish between two sub-images having the same CBC. LP is the sum of transitions of neighbors' weights, from sub-image center value to one and vice versa. Finally, creating histograms for both CBC and LP, then blending them to represent a robust local feature vector. Which can use for diagnosing, detecting. © 2021 Institute of Advanced Engineering and Science. All rights reserved.
dc.identifier.doi10.11591/ijece.v11i6.pp4972-4981
dc.identifier.endpage4981
dc.identifier.issn2088-8708
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85111156685
dc.identifier.scopusqualityQ2
dc.identifier.startpage4972
dc.identifier.urihttps://doi.org/10.11591/ijece.v11i6.pp4972-4981
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1428
dc.identifier.volume11
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Advanced Engineering and Science
dc.relation.ispartofInternational Journal of Electrical and Computer Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_Scopus_20250316
dc.subjectComputer vision
dc.subjectFeature extraction
dc.subjectLocal information pattern
dc.subjectMachine learning
dc.titleLocal information pattern descriptor for corneal diseases diagnosis
dc.typeArticle

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