[18 F]flutemetamol clinical trials have used visual assessment for dichotomous categorization of scans. However, quantification will provide additional information about level of tracer uptake and may also help enable consistency in image assessment over time and across different centers. In this study, we investigate the performance of a fully automated quantification method by calculating sensitivity and specificity against histopathology and by comparing scan categorization using quantification and visual evaluation, respectively. [18 F]Flutemetamol images from 10 prior studies were used. A total of 276 scans were used from 172 AD, MCI and healthy volunteer (HV) subjects, 36 normal pressure hydrocephalus (NPH) subjects, and 68 end-of-life subjects from an autopsy trial, where a histopathology standard of truth (SOT) was available. Five visual readers, trained with an electronic training program, categorized all scans. A fully automated PET-only, MNI-space, quantification method was used to compute SUVR values for a composite neocortical region using pons as reference. An SUVR threshold was derived by ranking the PET scans from the autopsy cohort based on the composite SUVR and comparing data with SOT, a dichotomous Bielschowsky silver stain assessment of neuritic plaques based on CERAD criteria. The derived threshold was used to categorize all 276 scans into normal and abnormal. For the 68 scans from end-of-life subjects, sensitivity and specificity against SOT were computed. For all 276 scans quantification results were compared to categorization using visual assessment. In the autopsy cohort, classification by quantification gave three false-positive and four false-negative scans, yielding 91% sensitivity and 88% specificity. All three false-positive cases were either borderline normal by SOT or had moderate to heavy cortical diffuse plaque burden. There was concordance between quantitative and visual read categorization for 268 of the 276 scans (97.1%). After excluding the autopsy scans, many of which were atypical with gross atrophy making both visual assessment and quantification difficult, quantitative and visual read categorization agreed in 206 of 208 remaining scans (99.0%). Categorization of [18 F]flutemetamol amyloid imaging data using an automated PET-only quantification method showed good agreement with histopathological classification of neuritic plaque density, and there was a strong concordance with visual read results.