The purpose of this study was to develop of kinetic analysis method for PACS management and computer-aided diagnosis. We obtained dynamic chest radiographs (512x512, 8bit, 4fps, and 1344x1344, 12bit, 3fps) of five healthy volunteers during respiration using an I.I. system twice, and one healthy volunteer using dynamic FPD system. Optical flows of images were obtained using customized block matching technique, and were divided into a direction, and transformed into the RGB color. Density was determined by the sum pixel length of movement during respiration phase. The made new static image was defined as the "kinetic map". The evaluation of patient's collation was performed with a template matching to the three colors. The same person's each correlation value and similar-coefficient which is defined in this study were statistically significant high (P<0.01). We used the artificial neural network (ANN) for the judgment of the same person. Five volunteers were divided into two groups, three volunteers and two volunteers became a training signal and unknown signal. Correlation value and similar-coefficient was used for the input signal, and ANN was designed so that the same person's probability might be outputted. The average of the specificity of the unknown signal obtained 98.2%. The kinetic map including the imitation tumor was used for the simulation. The tumor was detected by temporal subtraction of kinetic map, and then the superior sensitivity was obtained. Our analysis method was useful in risk management and computer-aided diagnosis.