title: Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles.
abstract: An automated diagnosis system that uses complex continuous wavelet transform (CWT) to process retina digital images and support vector machines (SVMs) for classification purposes is presented. In particular, each retina image is transformed into two one-dimensional signals by concatenating image rows and columns separately. The mathematical norm of phase angles found in each one-dimensional signal at each level of CWT decomposition are relied on to characterise the texture of normal images against abnormal images affected by exudates, drusen and microaneurysms. The leave-one-out cross-validation method was adopted to conduct experiments and the results from the SVM show that the proposed approach gives better results than those obtained by other methods based on the correct classification rate, sensitivity and specificity.
authors.affiliation: Department of Electrical Engineering , École de technologie supérieure , Montréal , Québec , H3C 1K3 , Canada.
authors.forename: Salim
authors.initials: S
authors.lastname: Lahmiri
authors.affiliation: Department of Electrical Engineering , École de technologie supérieure , Montréal , Québec , H3C 1K3 , Canada.
authors.forename: Christian S
authors.initials: CS
authors.lastname: Gargour
authors.affiliation: Department of Electrical Engineering , École de technologie supérieure , Montréal , Québec , H3C 1K3 , Canada.
authors.forename: Marcel
authors.initials: M
authors.lastname: Gabrea
keywords: CWT decomposition; SVM; automated diagnosis system; automated pathology detection; complex continuous wavelet transform phase angles; drusen; exudates; eye; image classification; image columns; image rows; image texture; leave-one-out cross-validation method; medical image processing; microaneurysms; one-dimensional signals; retina digital image; support vector machines; wavelet transforms