# High Accuracy Classification of Parkinson's Disease through Shape Analysis and Surface Fitting in 123I-Ioflupane SPECT Imaging

arXiv: https://arxiv.org/abs/1703.01526 · HTML: https://mlsanity.com/paper/1703.01526/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2017.

## Built on by (most cited first, 5 of 5)

- [Dopamine Transporter SPECT Image Classification for Neurodegenerative Parkinsonism via Diffusion Maps and Machine Learning Classifiers](https://mlsanity.com/paper/2104.02066/) (2021)
- [Self-normalized Classification of Parkinson's Disease DaTscan Images](https://mlsanity.com/paper/2112.13637/) (2021)
- [Predicting Parkinson's disease evolution using deep learning](https://mlsanity.com/paper/2312.17290/) (2023)
- [Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction](https://mlsanity.com/paper/2410.23649/) (2024)
- [Accurate early detection of Parkinson's disease from SPECT imaging through Convolutional Neural Networks](https://mlsanity.com/paper/2412.05348/) (2024)

## Abstract (arXiv)

Early and accurate identification of parkinsonian syndromes (PS) involving presynaptic degeneration from non-degenerative variants such as Scans Without Evidence of Dopaminergic Deficit (SWEDD) and tremor disorders, is important for effective patient management as the course, therapy and prognosis differ substantially between the two groups. In this study, we use Single Photon Emission Computed Tomography (SPECT) images from healthy normal, early PD and SWEDD subjects, as obtained from the Parkinson's Progression Markers Initiative (PPMI) database, and process them to compute shape- and surface fitting-based features for the three groups. We use these features to develop and compare various classification models that can discriminate between scans showing dopaminergic deficit, as in PD, from scans without the deficit, as in healthy normal or SWEDD. Along with it, we also compare these features with Striatal Binding Ratio (SBR)-based features, which are well-established and clinically used, by computing a feature importance score using Random forests technique. We observe that the Support Vector Machine (SVM) classifier gave the best performance with an accuracy of 97.29%. These features also showed higher importance than the SBR-based features. We infer from the study that shape analysis and surface fitting are useful and promising methods for extracting discriminatory features that can be used to develop diagnostic models that might have the potential to help clinicians in the diagnostic process.