Visual Scanning, Pattern Recognition and Decision-making in Pulmonary Nodule Detection

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Abstract
Eye movements were recorded while four subjects searched a set of 60 films, 24 normal and 36 abnormal for pulmonary nodules. Error rates, scanning patterns and the dwell time of fixation clusters on normal and nodule-containing areas of the film were studied. Using the assumption that prolonged dwell time indicates intensive processing of visual data, a model was developed for nodule detection that includes four steps: orientation, scanning, pattern recognition and decision-making. False-negative errors were divided into three classes: scanning errors, recognition errors and decision-making errors. Of 20 false-negative errors, 30% were considered scanning, 25% recognition and 45% decision-making.

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