A study on computer‐aided diagnosis based on temporal subtraction of sequential chest radiographs (in Japanese)

Abstract

An automated digital image subtraction technique for use with pairs of temporally sequential chest radiographs has been developed to aid radiologists in the detection of interval changes. Automated image registration based on nonlinear geometric warping is performed prior to subtraction in order to deal with complicated radiographic misregistration. Processing includes global matching, to achieve rough registration between the entire lung fields in the two images, and local matching, based on a cross‐correlation method, to determine local shift values for a number of small regions. A proper warping of is determined by fitting two‐dimensional polynomials to the distributions of the shift values. One image is warped and then subtracted from the other. The resultant subtraction images were able to enhance the conspicuity of various types of interval changes. Improved global matching based on a weighted template matching method achieved robust registration even with photofluorographs taken in chest mass screening programs, which had previously presented us with a relatively large number of poor‐registration images. The new method was applied to 129 pairs of chest mass screening images, and offered registration accuracy as good as manual global matching. An observer test using 114 cases including 57 lung cancer cases presented better sensitivity and specificity on average compared to conventional comparison readings. In addition, newly developed image processing that eliminates the rib edge artifacts in subtraction images was applied to 26 images having pathological interval changes; results showed the potential for application to automated schemes for the detection of interval change patterns. With its capacity to improve the diagnostic accuracy of chest radiographs, the chest temporal subtraction technique promises to become an important element of computer‐aided diagnosis (CAD) systems.