Automatic Detection of Lung Nodules in Temporal Subtraction Image by Use of Shape and Density Features

Computer aided diagnosis is one of the most important tools for supporting system on visual screening in medical fields. However, sensitivity for detection of small nodules is unsatisfactory. It is because detection of subtle lesions on computed tomography (CT) images is a difficult task for radiologists. Recently as one of solutions to avoid the problem, a temporal subtraction technique is introduced. The temporal subtraction image is obtained by subtraction of a previous image from a current one, and can be used for enhancing interval changes on medical images by removing most of the normal background structures. In this study, we have developed a new method for automatic detection of lung nodules based on artificial neural networks from a temporal subtraction image. First, the candidates for nodules were detected by use of a multiple threshold technique based on the pixel value in the temporal subtraction images obtained by the voxel-matching technique. Next, false positives of nodules candidate are removed by use of selective enhancement filter and a rule-based method with classifier based on artificial neural networks. We applied our computerized scheme to 6 MDCT cases including 87 lung nodules. Our scheme for detecting lung nodules provided a sensitivity of 80.5% for lung nodules with sizes less than 20mm, and with 7.5 false positives per scan. In this paper, we discussed the experimental result of detection and statistical features.