Self-normalized Classification of Parkinson's Disease DaTscan Images
This addresses variability in Parkinson's disease diagnosis from DaTscan images, offering a more standardized approach, though it appears incremental as it builds on existing classification methods by removing a preprocessing step.
The paper tackled the problem of variability in classifying SPECT images due to non-standard normalization regions by proposing a self-normalized classification strategy that eliminates the need for normalization. It applied this method to DaTscan images from 573 subjects, achieving classification and understanding progression from baseline to year 4.
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.