Comparison of the quality of temporal subtraction images obtained with manual and automated methods of digital chest radiography

The authors have been developing a fully automated temporal subtraction scheme to assist radiologists in the detection of interval changes in digital chest radiographs. The temporal subtraction image is obtained by subtraction of a previous image from a current image. The authors' automated method includes not only image shift and rotation techniques but also a nonlinear geometric warping technique for reduction of misregistration artifacts in the subtraction image. However, a manual subtraction method that can be carried out only with image shift and rotation has been employed as a common clinical technique in angiography, and it might be clinically acceptable for detection of interval changes on chest radiographs as well. Therefore, the authors applied both the manual and automated temporal subtraction techniques to 181 digital chest radiographs, and compared the quality of the subtraction images obtained with the two methods. The numbers of clinically acceptable subtraction images were 147 (81.2%) and 176 (97.2%) for the manual and automated subtraction methods, respectively. The image quality of 148 (81.8%) subtraction images was improved by use of the automated method in comparison with the subtraction images obtained with the manual method. These results indicate that the automated method with the nonlinear warping technique can significantly reduce misregistration artifacts in comparison with the manual method. Therefore, the authors believe that the automated subtraction method is more useful for the detection of interval changes in digital chest radiographs.

Application of temporal subtraction for detection of interval changes on chest radiographs: improvement of subtraction images using automated initial image matching

The authors developed a temporal subtraction scheme based on a nonlinear geometric warping technique to assist radiologists in the detection of interval changes in chest radiographs obtained on different occasions. The performance of the current temporal subtraction scheme is reasonably good; however, severe misregistration can occur in some cases. The authors evaluated the quality of 100 chest temporal subtraction images selected from their clinical image database. Severe misregistration was mainly attributable to initial incorrect global matching. Therefore, they attempted to improve the quality of the subtraction images by applying a new initial image matching technique to determine the global shift value between the current and the previous chest images. A cross-correlation method was employed for the initial image matching by use of blurred low-resolution chest images. Nineteen cases (40.4%) among 47 poor registered subtraction images were improved. These results show that the new initial image matching technique is very effective for improving the quality of chest temporal subtraction images, which can greatly enhance subtle changes in chest radiographs.