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 topographicalimages measured, and assessed by an ophthalmologist. Consequently, an important priority is the early and accuratediagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms. Images producedby a Pentacam device can be subjected to rotation or some distortion during acquisition; therefore, accurate diagnosisrequires the use of local features in the image. Accordingly, a new algorithm called subbands wavelet for local featurestransform (SWFT) which is mainly based on the algorithm of a scale-invariant feature transform (SIFT) has beendeveloped. This algorithm uses wavelets as a multiresolution analysis to produce images with different scales instead ofusing the difference of Gaussians as in the SIFT algorithm. The experimental results on the corneal topography datasetindicate that the proposed SWFT outperforms the baseline SIFT algorithm.
Computer-aided diagnosis, feature extraction, machine learning, support vector machines, wavelet trans-forms
AL-SALIHI, SAMER; AYDIN, SEZGİN; and HUSSEIN, NEBRAS
"SWFT: Subbands wavelet for local features transform descriptor for cornealdiseases diagnosis,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 29:
2, Article 26.
Available at: https://journals.tubitak.gov.tr/elektrik/vol29/iss2/26