Early detection of Parkinson's disease through shape based features from <sup>123</sup>I-Ioflupane SPECT imaging

Abstract

Detection of Parkinson's disease (PD) at an early stage is important for effective management and for initiating neuroprotective strategies early in the therapeutic process. Single photon emission computed tomography (SPECT) using 123I-Ioflupane (DaTSCANTM, GE Healthcare; also known as [123I]FP-CIT) have shown to be a sensitive marker for PD even in the early stages of the disease. In this paper, we carry out image processing to compute shape-based features which are radial and gradient features from SPECT scans from 163 early-stage PD and 187 healthy normal subjects obtained from the Parkinson's Progression Markers Initiative (PPMI), and use them along with the striatal binding ratio (SBR) values, also provided by the PPMI as features to classify between the two using Discriminant Analysis and Support Vector Machine (SVM). We observe a high accuracy of 99.42% in classification. It is inferred that such models can aid clinicians in the early diagnostics of PD.