  Neurodegenerative parkinsonism can be assessed by dopamine transporter single
photon emission computed tomography (DaT-SPECT). Although generating images is
time consuming, these images can show interobserver variability and they have
been visually interpreted by nuclear medicine physicians to date. Accordingly,
this study aims to provide an automatic and robust method based on Diffusion
Maps and machine learning classifiers to classify the SPECT images into two
types, namely Normal and Abnormal DaT-SPECT image groups. In the proposed
method, the 3D images of N patients are mapped to an N by N pairwise distance
matrix and are visualized in Diffusion Maps coordinates. The images of the
training set are embedded into a low-dimensional space by using diffusion maps.
Moreover, we use Nystr\"om's out-of-sample extension, which embeds new sample
points as the testing set in the reduced space. Testing samples in the embedded
space are then classified into two types through the ensemble classifier with
Linear Discriminant Analysis (LDA) and voting procedure through
twenty-five-fold cross-validation results. The feasibility of the method is
demonstrated via Parkinsonism Progression Markers Initiative (PPMI) dataset of
1097 subjects and a clinical cohort from Kaohsiung Chang Gung Memorial Hospital
(KCGMH-TW) of 630 patients. We compare performances using Diffusion Maps with
those of three alternative manifold methods for dimension reduction, namely
Locally Linear Embedding (LLE), Isomorphic Mapping Algorithm (Isomap), and
Kernel Principal Component Analysis (Kernel PCA). We also compare results using
2D and 3D CNN methods. The diffusion maps method has an average accuracy of 98%
for the PPMI and 90% for the KCGMH-TW dataset with twenty-five fold
cross-validation results. It outperforms the other three methods concerning the
overall accuracy and the robustness in the training and testing samples.
