High-frequency-based features for low and high retina haemorrhage classification

Healthcare Technology Letters

Salim Lahmiri¹ 

[1] École de Technologie Supérieure;

关键词: eye;    image classification;    diseases;    medical image processing;    biomedical optical imaging;    discrete wavelet transforms;    support vector machines;    variational mode decomposition;    empirical mode decomposition;    discrete wavelet transform;    support vector machine;    multiresolution analysis technique;    diabetic retinopathy;    fundus images;    high retina haemorrhage classification;    low retina haemorrhage classification;   

DOI  :  10.1049/htl.2016.0067

来源: DOAJ

【 摘 要 】

Haemorrhages (HAs) presence in fundus images is one of the most important indicators of diabetic retinopathy that causes blindness. In this regard, accurate grading of HAs in fundus images is crucial for appropriate medical treatment. The purpose of this Letter is to assess the relative performance of statistical features obtained with three different multi-resolution analysis (MRA) techniques and fed to support vector machine in grading retinal HAs. Considered MRA techniques are the common discrete wavelet transform (DWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD). The obtained experimental results show that statistical features obtained by EMD, VMD, and DWT, respectively, achieved 88.31% ± 0.0832, 71% ± 0.1782, and 64% ± 0.0949 accuracies. It also outperformed VMD and DWT in terms of sensitivity and specificity. Thus, the EMD-based features are promising for grading retinal HAs.

【 授权许可】

Unknown