In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD),
poor cognition, inflammation and hypertension. We propose a fully automatic scheme that
uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia
(BG) region as low or high. We assess the performance of three different types of descriptors
extracted from the BG region in T2-weighted MRI images: (i) statistics obtained
from Wavelet transform’s coefficients, (ii) local binary patterns and (iii) bag of visual words
(BoW) based descriptors characterizing local keypoints obtained from a dense grid with the
scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM
classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW
descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer
1) and by a trained image analyst (Observer 2). The agreement and cross-correlation
between the classifier and Observer 2 (κ = 0.67 (0.58–0.76)) were slightly higher than between
the classifier and Observer 1 (κ = 0.62 (0.53–0.72)) and comparable between both
the observers (κ = 0.68 (0.61–0.75)). Finally, three logistic regression models using clinical
variables as independent variable and each of the PVS ratings as dependent variable
were built to assess how clinically meaningful were the predictions of the classifier. The
goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values:
0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02
units higher) than that of the model for Observer 2. These results suggest that, although it
can be improved, an automatic classifier to assess PVS burden from brain MRI can provide
clinically meaningful results close to those from a trained observer
