Bias field correction of chest thin section CT images

Abstract

Helical computed tomography (CT) is a promising tool for the early diagnosis of lung cancer. The three-dimensional information makes it possible to detect a subtle change in any field of the lung. However, the diagnostic procedure is time-consuming, since a considerable number of images have to be reviewed in one examination. In order to lessen the burden to the reviewing physician and to improve the accuracy of diagnosis, we are developing a computer system, by which shadows of diagnostic importance can be highlighted among a number of nuisance changes. In particular, the peripheral blood vessels are analyzed with a special focus on the changes caused by lung cancer. We developed a computer algorithm, by which pulmonary blood vessels are extracted after removing the background bias. The comparison between the computer algorithm and an expert physician's reading showed a good agreement. Furthermore, this system can provide temporal changes in blood vessels, which are extremely important in diagnosis.