Abstract OBJECTIVE: In diagnostic imaging; human perception is the most prominent, yet least studied, source of error. A better understanding of image perception will help to improve diagnostic performance. This study focuses on the perception of coarseness of trabecular patterns on dental radiographs. Comparison of human vision with machine vision should yield knowledge on human perception. METHOD: In a study on identifying osteoporotic patients, dental radiographs were made from 505 post-menopausal women aged 45-70 years. Intra-oral radiographs of the lower and upper jaws were made. Five observers graded the trabecular pattern as dense, sparse or mixed. The five gradings were combined into a single averaged observer score per jaw. The radiographs were scanned and a region of interest (ROI) was indicated on each. The ROIs were processed with image analysis software measuring 25 image features. Pearson correlation and multiple linear regression were used to compare the averaged observer score with the image features. RESULTS: 14 image features correlated significantly with the observer judgement for both jaws. The strongest correlation was found for the average grey value in the ROI. Other features, describing that osteoporotic patients have fewer but bigger marrow spaces than controls, correlated less with the sparseness of the trabecular pattern than a rather crude measure for structure such as the average grey value. CONCLUSION: Human perception of the sparseness of trabecular patterns is based more on average grey values of the ROI than on geometric details within the ROI.