Development of Similar Image Subtraction Method for Digital Chest Radiographs

我们开发了一种类似图像减影方法的计算机化方案，它提供了从不同患者的相似胸片中获得的减影图像。1.以岩手县肺癌筛查获得的8776张正常胸片为研究对象，建立了大型影像数据库。所有图像均来自计算机X线摄影(CR)系统。2.从由正常胸片组成的大型数据库中搜索目标图像的相似图像。首先，根据目标图像和正常图像肺大小的相似度，选择相似图像(数据库中10%)的初始候选者。然后，利用目标图像与候选图像之间的相关值，从初始候选图像中进一步选取相似图像；最后，将选取的不同患者的相似图像从目标图像中减去，得到减影图像。采用基于非线性图像扭曲的全局匹配和局部匹配相结合的方法进行减影。4.对不同患者的相似胸片的减影图像质量进行主观评价。5.从数据库中选取相似图像，使其与目标胸片具有显著的相似性。虽然从不同患者获得的减影图像中包含一些伪影，但它们的图像质量对于异常病变的检测是可以接受的。从相似图像中获得的减影图像中，约有40%被认为对增强胸部X线片中的异常有临床意义。

We have developed a computerized scheme of similar image subtraction method which provides subtraction images obtained from similar chest radiographs of different patients. In this study, an image to be diagnosed is called "target image".1.A large image database was established with 8776 normal chest radiographs(5352 female images and 3424 male images) obtained from a lung cancer screening in Iwate prefecture. All images were taken from a computed radiography(CR) system.2.Similar images of a target image were searched from a large database consist of normal chest radiographs. First, initial candidates of the similar image(10% in the database) were selected by the similarity of the lung size between the target image and normal images. Second, further similar images were chosen from the initial candidates by using their correlation values between the target image and candidates.3.Finally, subtraction images were produced by subtracting the selected similar images of different patients from the target image. The subtraction was performed with a global matching and local matching based on a nonlinear image warping technique.4.The quality of the subtraction images obtained from similar chest radiographs of different patients were evaluated subjectively in terms of artifacts due to misregistration between a target image and a similar image.5.Similar images selected from the database were remarkably resembled to the target chest radiographs. Although the subtracted images obtained from different patients included some artifacts, their image qualities were acceptable for detection of abnormal lesions. Approximately, 40% of the subtraction images obtained from the similar images were considered clinically useful to enhance abnormalities in chest radiographs.