  Normal Pressure Hydrocephalus (NPH) is one of the few reversible forms of
dementia, Due to their low cost and versatility, Computed Tomography (CT) scans
have long been used as an aid to help diagnose intracerebral anomalies such as
NPH. However, no well-defined and effective protocol currently exists for the
analysis of CT scan-based ventricular, cerebral mass and subarachnoid space
volumes in the setting of NPH. The Evan's ratio, an approximation of the ratio
of ventricle to brain volume using only one 2D slice of the scan, has been
proposed but is not robust. Instead of manually measuring a 2-dimensional proxy
for the ratio of ventricle volume to brain volume, this study proposes an
automated method of calculating the brain volumes for better recognition of NPH
from a radiological standpoint. The method first aligns the subject CT volume
to a common space through an affine transformation, then uses a random forest
classifier to mask relevant tissue types. A 3D morphological segmentation
method is used to partition the brain volume, which in turn is used to train
machine learning methods to classify the subjects into non-NPH vs. NPH based on
volumetric information. The proposed algorithm has increased sensitivity
compared to the Evan's ratio thresholding method.
