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Content Provider | IET Digital Library |
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Author | Lahmiri, Salim |
Abstract | 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. |
Starting Page | 20 |
Ending Page | 24 |
Page Count | 5 |
Volume Number | 4 |
e-ISSN | 20533713 |
Issue Number | Issue 1, Feb (2017) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/htl/4/1 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/htl.2016.0067 |
Journal | Healthcare Technology Letters |
Publisher Date | 2017-02-16 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Anatomy And Optics of The Eye Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging Computer Vision And Image Processing Technique Diabetic Retinopathy Discrete Wavelet Transform Diseases Empirical Mode Decomposition Eye Fundus Image High Retina Haemorrhage Classification Image Classification Integral Transforms Knowledge Engineering Technique Low Retina Haemorrhage Classification Medical Image Processing Multiresolution Analysis Technique Optical And Laser Radiation Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Support Vector Machine Variational Mode Decomposition |
Content Type | Text |
Resource Type | Article |
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