access icon openaccess Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles

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.

Inspec keywords: image classification; support vector machines; eye; image texture; medical image processing; wavelet transforms

Other keywords: image rows; leave-one-out cross-validation method; image columns; automated diagnosis system; image texture; automated pathology detection; exudates; SVM; complex continuous wavelet transform phase angles; support vector machines; microaneurysms; CWT decomposition; image classification; drusen; retina digital image; one-dimensional signals

Subjects: Patient diagnostic methods and instrumentation; Biomedical measurement and imaging; Biology and medical computing; Knowledge engineering techniques; Medical and biomedical uses of fields, radiations, and radioactivity; health physics; Computer vision and image processing techniques; Image recognition; Integral transforms; Physiological optics, vision; Integral transforms; Function theory, analysis

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