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

被引：14

作者：

Lahmiri, Salim ^{[1
,2
]}

机构：

[1] Department of Electrical Engineering, École de Technologie Supérieure, Montreal, Canada

[2] CENPARMI, Concordia University, Montreal, Canada

来源：

Healthcare Technology Letters | 2017年 / 4卷 / 01期

关键词：

Wavelet decomposition - Signal reconstruction - Support vector machines - Variational techniques - Grading - Eye protection - Medical imaging - Ophthalmology - Discrete wavelet transforms - Empirical mode decomposition;

D O I：

10.1049/htl.2016.0067

中图分类号：

学科分类号：

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. © The Institution of Engineering and Technology 2017.

引用

页码：20 / 24