Classification method based on surf and sift features for alzheimer diagnosis using diffusion tensor magnetic resonance imaging

Topic: Amyloid, tau and blood-test diagnosis

weighted MRI can assess macro-structural brain atrophy, diffusion tensor imaging (DTI) unveils these hidden microstructural alterations. This study explores the use of DTI data, specifically visual patterns in Fractional Anisotropy (FA), Mean Diffusivity (MD), and Radial Diffusivity (RD) maps, to characterize AD progression. This paper proposes a computer-aided diagnosis (CAD) framework employing SIFT and SURF descriptors and a bag-of-words approach to build AD-specific signatures for the hippocampus region, known to be heavily affected by the disease. These signatures are extracted from MD, FA, and RD maps and used to differentiate between AD, mild cognitive impairment (MCI), and normal controls (NC) in both multiclass and binary classification scenarios. Additionally, we investigate late fusion of visual map features for enhanced decision-making. The experiments were accomplished with a subset of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset formed of AD patients (n = 35), Early Mild Cognitive Impairment (EMCI) (n = 6), Late Mild Cognitive Impairment (LMCI) (n = 24) and cognitively healthy elderly Normal Controls (NC) (n = 31). Promising preliminary results demonstrate the potential of the proposed system as a useful tool to capture the AD leanness with achieving accuracies of 87.5%, 87.4%, 89%, and 95.2% for MD, FA, RD, and fusion of features respectively for the multiclass system using SIFT features. Using FA features for binary discrimination achieves 97.5%. Moreover, the fusion based on the decision level model reached an accuracy of 93.3% AD/MCI, 95.7% AD/NC, and 93.3% MCI/NC (96.2 ± 3.6 MCI vs. NC, 97.5 ± 5 AD vs. NC). Furthermore, fusion of features led to a noteworthy precision boost of 96%. These findings suggest that our DTI-based CAD framework holds promise as a reliable and accurate tool for capturing AD progression, paving the way for earlier diagnosis and potentially improved patient outcomes.

Key findings

• Promising preliminary results demonstrate the potential of the proposed system as a useful tool to capture the AD leanness with achieving accuracies of 87.5%, 87.4%, 89%, and 95.2% for MD, FA, RD, and fusion of features respectively for the multiclass system using SIFT features.
• These findings suggest that our DTI-based CAD framework holds promise as a reliable and accurate tool for capturing AD progression, paving the way for earlier diagnosis and potentially improved patient outcomes.

Why it matters

This study describes a computer-aided diagnosis framework using diffusion tensor imaging features to distinguish Alzheimer's disease, mild cognitive impairment, and normal controls. The authors describe these as promising preliminary results from a small subset of participants, and the work is about diagnosis rather than treatment.

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Study details

• Participants35
• Effect size87.5%; 87.4%; 95.2%
• Evidence strengthLow

Read the original

• DOI: 10.1038/s41598-025-92759-2
• PubMed: 40118994
• Original source →
• Free full text: Scientific Reports →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{zayed2025-40118994,
  author  = {Zayed, Nourhan Mohamed and Eldeep, Ghaidaa and Yassine, Inas A.},
  title   = {{Classification method based on surf and sift features for alzheimer diagnosis using diffusion tensor magnetic resonance imaging}},
  journal = {Scientific Reports},
  year    = {2025},
  doi     = {10.1038/s41598-025-92759-2},
  pmid    = {40118994},
  url     = {https://doi.org/10.1038/s41598-025-92759-2}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages NeuroImage · 2017-04-13 · usefulness score 56/100 · Mild cognitive impairment
• Brain imaging in Alzheimer disease Cold Spring Harbor perspectives in medicine · 2012-04-01 · usefulness score 53/100 · Getting a diagnosis
• Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data PloS one · 2013-05-31 · usefulness score 52/100 · Digital health tools

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• Visual assessment of atrophy on magnetic resonance imaging in the diagnosis of pathologically confirmed young-onset dementias Archives of neurology · 2005-09-01 · usefulness score 43/100
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• Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease: power calculations and estimates of sample size to detect treatment effects Archives of neurology · 2000-03-01 · usefulness score 53/100
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• Presymptomatic cortical thinning in familial Alzheimer disease: A longitudinal MRI study Neurology · 2016-10-12 · usefulness score 62/100 · Brain scans and imaging
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