SWFT: Subbands wavelet for local features transform descriptor for corneal diseases diagnosis
Yükleniyor...
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Turkiye Klinikleri
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Human 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.
Açıklama
Al-Salihi, Samer K. (Aksaray, Yazar )
Anahtar Kelimeler
Computer-aided Diagnosis, Feature Extraction, Machine Learning, Support Vector Machines, Wavelet Transforms
Kaynak
Turkish Journal of Electrical Engineering and Computer Sciences
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
29
Sayı
2
