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

TL;DR

This study develops a high-accuracy classification method for Parkinson's disease using shape analysis and surface fitting of SPECT images, outperforming traditional features and aiding clinical diagnosis.

Contribution

The paper introduces a novel approach combining shape and surface fitting features for Parkinson's classification, demonstrating superior accuracy over traditional SBR-based features.

Findings

Support Vector Machine achieved 97.29% accuracy.

Shape and surface features outperformed SBR-based features.

Features showed potential for aiding clinical diagnosis.

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.