Title: High Accuracy Classification of Parkinson's Disease through Shape Analysis and Surface Fitting in $^{123}$I-Ioflupane SPECT Imaging,
Abstract: 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.