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

Healthcare Technology Letters

article

Salim Lahmiri¹  Christian S. Gargour¹  Marcel Gabrea¹ 

[1] Department of Electrical Engineering

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

DOI  :  10.1049/htl.2014.0068

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

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.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND