Self-normalized Classification of Parkinson's Disease DaTscan Images

Document detail

ID

oai:arXiv.org:2112.13637

Topic

Electrical Engineering and Systems... Computer Science - Computer Vision...

Author

Zhou, Yuan Tagare, Hemant D.

Year

2021

listing date

3/31/2022

Keywords

parkinson using region normalization images

Metrics

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.

;Comment: To appear in IEEE BIBM 2021

Zhou, Yuan,Tagare, Hemant D., 2021, Self-normalized Classification of Parkinson's Disease DaTscan Images