SWFT: Subbands wavelet for local features transform descriptor for corneal diseases diagnosis

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dc.authoridKais, samer/0000-0003-2236-9303
dc.authoridAYDIN, Sezgin/0000-0001-5380-1512
dc.contributor.authorAl-Salihi, Samer K.
dc.contributor.authorAydin, Sezgin
dc.contributor.authorGhaeb, Nebras H.
dc.date.accessioned2025-03-17T12:25:11Z
dc.date.available2025-03-17T12:25:11Z
dc.date.issued2021
dc.departmentTarsus Üniversitesi
dc.description.abstractHuman cornea is the front see-through shield of the eye. It refracts light onto the retina to induce vision. Therefore, any defect in the cornea may lead to vision disturbance. This deficiency is estimated by sets of topographical images measured, and assessed by an ophthalmologist. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms. Images produced by a Pentacam device can be subjected to rotation or some distortion during acquisition; therefore, accurate diagnosis requires the use of local features in the image. Accordingly, a new algorithm called subbands wavelet for local features transform (SWFT) which is mainly based on the algorithm of a scale-invariant feature transform (SIFT) has been developed. This algorithm uses wavelets as a multiresolution analysis to produce images with different scales instead of using the difference of Gaussians as in the SIFT algorithm. The experimental results on the corneal topography dataset indicate that the proposed SWFT outperforms the baseline SIFT algorithm.
dc.identifier.doi10.3906/elk-2004-114
dc.identifier.endpage896
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85104703753
dc.identifier.scopusqualityQ2
dc.identifier.startpage875
dc.identifier.trdizinid514943
dc.identifier.urihttps://doi.org/10.3906/elk-2004-114
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/514943
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1539
dc.identifier.volume29
dc.identifier.wosWOS:000680006300003
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectComputer-aided diagnosis
dc.subjectfeature extraction machine learning support vector machines
dc.subjectwavelet transforms
dc.titleSWFT: Subbands wavelet for local features transform descriptor for corneal diseases diagnosis
dc.typeArticle

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