Lesion removal and lesion addition algorithms in lung volumetric data sets for perception studies

摘要

Image perception studies of medical images provide important information about how radiologists interpret images and insights for reducing reading errors. In the past, perception studies have been difficult to perform using clinical imaging studies because of the problems associated with obtaining images demonstrating proven abnormalities and appropriate normal control images. We developed and evaluated interactive software that allows the seamless removal of abnormal areas from CT lung image sets. We have also developed interactive software for capturing lung lesions in a database where they can be added to lung CT studies. The efficacy of the software to remove abnormal areas of lung CT studies was evaluated psychophysically 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 scaled to 3 levels of confidence. The results of these experiments demonstrated that the radiologist had difficulty in distinguishing the raw clinical images from those that had been altered. We conclude that this software can be used to create experimental normal control and "proven" lesion data sets for volumetric CT of the lung fields. We also note that this software can be easily adapted to work with other tissue besides lung and that it can be adapted to other digital imaging modalities.