  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 interprete 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 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 through 2D and 3D CNN methods. The diffusion maps method has an
average accuracy of 98% from the PPMI and 90% from 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.
