A new software tool for removing, storing, and adding abnormalities to medical images for perception research studies

Image perception studies have been difficult to perform using clinical images because of the problems associated with obtaining proven abnormalities and appropriate normal controls. The objective of this research was to develop and evaluate interactive software that allows the seamless removal, archiving and insertion of abnormal areas from computed tomography (CT) lung image sets for use in image perception research.

The software tools for removing, archiving, and adding lesions are described in detail. The efficacy of the software to remove abnormal areas of lung CT studies was evaluated by having radiologists select the one altered image from a display of four. The software for adding lesions was evaluated by having radiologists classify displayed CT slices with lesions as real or artificial along with their confidence level.

Observers could not reliably detect when images had been altered by the software. In the lesion-removal experiment, the observers correctly identified the altered display in only 15.8 +/- 2.8 of 56 sets. In the lesion-add experiment, the observers correctly identified the artificially placed lesions in 38.2 +/- 3.9 of 77 sets. The frequency distribution of the correct responses did not differ from that expected from chance selection.

The results from both of these experiments demonstrate that radiologists could not distinguish between original and altered images. We conclude that this software can be used with volumetric CT lung images for creating normal control and target data sets for medical image perception research.