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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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High-frequency-based features for low and high retina haemorrhage classification.

Salim Lahmiri1,2

  • 1Department of Electrical Engineering, École de Technologie Supérieure, Montreal, Canada.

Healthcare Technology Letters
|May 23, 2017
PubMed
Summary

Empirical mode decomposition (EMD) features show promise for grading retinal hemorrhages (HAs) in fundus images, achieving 88.31% accuracy. This method outperforms discrete wavelet transform (DWT) and variational mode decomposition (VMD) for diabetic retinopathy detection.

Keywords:
biomedical optical imagingdiabetic retinopathydiscrete wavelet transformdiscrete wavelet transformsdiseasesempirical mode decompositioneyefundus imageshigh retina haemorrhage classificationimage classificationlow retina haemorrhage classificationmedical image processingmultiresolution analysis techniquesupport vector machinesupport vector machinesvariational mode decomposition

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Biomedical Engineering

Background:

  • Diabetic retinopathy is a leading cause of blindness.
  • Diabetic retinopathy diagnosis relies on identifying fundus image indicators like hemorrhages (HAs).
  • Accurate grading of HAs is critical for effective treatment.

Purpose of the Study:

  • To compare the performance of statistical features from three multi-resolution analysis (MRA) techniques for grading retinal HAs.
  • To evaluate discrete wavelet transform (DWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD) for HA grading.
  • To determine the most effective MRA technique for automated HA detection in fundus images.

Main Methods:

  • Extracted statistical features using DWT, EMD, and VMD.
  • Utilized a support vector machine (SVM) classifier for grading retinal HAs based on extracted features.
  • Performed comparative analysis of the MRA techniques' performance.

Main Results:

  • EMD-based features achieved the highest accuracy of 88.31% ± 0.0832.
  • VMD achieved 71% ± 0.1782 accuracy, and DWT achieved 64% ± 0.0949 accuracy.
  • EMD also demonstrated superior sensitivity and specificity compared to VMD and DWT.

Conclusions:

  • EMD-based statistical features are highly effective for grading retinal HAs.
  • EMD offers a promising approach for improving diabetic retinopathy screening and diagnosis.
  • This study highlights the potential of EMD in medical image analysis for ocular diseases.