ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2021
Self-normalized Classification of Parkinson's Disease DaTscan Images.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Hybrid deep learning novel framework for classification of parkinson's disease.Scientific reports · 2026Article
- Dopamine Transporter Imaging as Objective Monitoring Biomarker in Parkinson's Disease.Annals of neurology · 2025Article
- Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs.Frontiers in aging neuroscience · 2023Review
- Detecting Parkinson's Disease through Gait Measures Using Machine Learning.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
2 authors.
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Abstract
Classifying SPECT images requires a preprocessing step which normalizes the images using a normalization region. The choice of the normalization region is not standard, and using different normalization regions introduces normalization region-dependent variability. This paper mathematically analyzes the effect of the normalization region to show that normalized-classification is exactly equivalent to a subspace separation of the half rays of the images under multiplicative equivalence. Using this geometry, a new self-normalized classification strategy is proposed. This strategy eliminates the normalizing region altogether. The theory is used to classify DaTscan images of 365 Parkinson's disease (PD) subjects and 208 healthy control (HC) subjects from the Parkinson's Progression Marker Initiative (PPMI). The theory is also used to understand PD progression from baseline to year 4.
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Registered trials
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