Automatic Separation of Parkinsonian Patients and Control Subjects Based on the Striatal Morphology

Abstract

Parkinsonism is the second more common neurological disease and affects around 1%–2% of people over 65 years, being around 20%–24% of them incorrectly diagnosed. The disorder is associated to a progressive loss of dopaminergic neurons of the striatum. Thus, its diagnosis is usually corroborated by analyzing neuroimaging data of this region. In this work, we propose a novel computer system to automatically distinguish between parkinsonian patients and neurologically healthy subjects using $^{123}$I-FP-CIT SPECT data, a neuroimaging modality widely used to assist the diagnosis of Parkinsonism. First, the voxels of the striatum were selected using an intensity threshold. These voxels were then projected over the axial plane, resulting in a two-dimensional image with the striatum shape. Subsequently, the size and shape of the left and right sides of the striatum were characterized by 5 features: area, eccentricity, orientation and length of the major and minor axes. Finally, the extracted features were used along with a Support Vector Machine classifier to separate patients and controls. An accuracy rate of 91.53% ($p<0.001$) was estimated using a k-fold cross-validation scheme and a database with 189 $^{123}$I-FP-CIT SPECT neuroimages. This rate outperformed the ones achieved by previous approaches when using the same data.