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Related Experiment Video

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Classification method based on surf and sift features for alzheimer diagnosis using diffusion tensor magnetic

Nourhan Zayed1,2, Ghaidaa Eldeep3, Inas A Yassine3

  • 1Computer and Systems Department, Electronics Research Institute, Cairo, Egypt. nourhan@eri.sci.eg.

Scientific Reports
|March 22, 2025
PubMed
Summary

This study introduces a computer-aided diagnosis system using diffusion tensor imaging (DTI) to detect Alzheimer's disease (AD) progression. The DTI-based framework accurately identifies microstructural changes, aiding in earlier and more precise diagnosis of AD and mild cognitive impairment.

Keywords:
Alzheimer ’s disease (AD)AmygdalaBag of wordsDiffusion tensor imaging (DTI)HippocampusSIFT features and SURF features

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Biomedical Engineering

Background:

  • Alzheimer's disease (AD) diagnosis is challenging due to progressive microstructural brain changes not visible with standard MRI.
  • Diffusion Tensor Imaging (DTI) offers insights into these subtle alterations, crucial for early detection.

Purpose of the Study:

  • To develop and evaluate a computer-aided diagnosis (CAD) framework for characterizing Alzheimer's disease progression using DTI.
  • To differentiate between Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC) based on DTI-derived visual patterns.

Main Methods:

  • Utilized DTI data, specifically Fractional Anisotropy (FA), Mean Diffusivity (MD), and Radial Diffusivity (RD) maps.
  • Employed SIFT and SURF feature descriptors with a bag-of-words model to create AD-specific signatures in the hippocampus.
  • Investigated multiclass and binary classification, including late fusion of visual map features for improved decision-making.

Main Results:

  • The proposed DTI-based CAD system achieved high accuracies, with feature fusion reaching 95.2% in multiclass classification.
  • Binary classification using FA features attained 97.5% accuracy.
  • Decision-level fusion demonstrated robust performance, with accuracies up to 97.5% for AD/NC discrimination and a precision boost of 96%.

Conclusions:

  • The DTI-based CAD framework shows significant potential as a reliable tool for detecting Alzheimer's disease progression.
  • The system's ability to capture microstructural changes suggests promise for earlier diagnosis and improved patient outcomes.
  • Feature fusion techniques enhance diagnostic accuracy, highlighting the value of integrating multiple DTI-derived metrics.