Open AccessAutomated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles
Author(s) -
Lahmiri Salim,
Gargour Christian S.,
Gabrea Marcel
Publication year - 2014
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2014.0068
Subject(s) - artificial intelligence , support vector machine , pattern recognition (psychology) , computer vision , computer science , continuous wavelet transform , wavelet , wavelet transform , image processing , complex wavelet transform , digital image , sensitivity (control systems) , discrete wavelet transform , image (mathematics) , electronic engineering , engineering
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.