Evidence suggests that normal pressure hydrocephalus (NPH) is underdiagnosed in day to day radiologic practice, and differentiating NPH from cerebral atrophy due to other neurodegenerative diseases and normal aging remains a challenge. To better characterize NPH, we test the hypothesis that a prediction model based on automated MRI brain tissue segmentation can help differentiate shunt-responsive NPH patients from cerebral atrophy due to Alzheimer disease (AD) and normal aging. Brain segmentation into gray and white matter (GM, WM), and intracranial cerebrospinal fluid was derived from pre-shunt T1-weighted MRI of 15 shunt-responsive NPH patients (9 men, 72.6 ± 8.0 years-old), 17 AD patients (10 men, 72.1 ± 11.0 years-old) chosen as a representative of cerebral atrophy in this age group; and 18 matched healthy elderly controls (HC, 7 men, 69.7 ± 7.0 years old). A multinomial prediction model was generated based on brain tissue volume distributions. GM decrease of 33% relative to HC characterized AD (P < 0.005). High preoperative ventricular and near normal GM volumes characterized NPH. A multinomial regression model based on gender, GM and ventricular volume had 96.3% accuracy differentiating NPH from AD and HC. In conclusion, automated MRI brain tissue segmentation differentiates shunt-responsive NPH with high accuracy from atrophy due to AD and normal aging. This method may improve diagnosis of NPH and improve our ability to distinguish normal from pathologic aging.