Development of an automated patient recognition method for chest CT images using a template-matching technique

Abstract

If patient information, such as identification number or patient name, has been entered incorrectly in a picture archiving and communication system (PACS) environment, the image may be stored in the wrong place. To prevent such cases of misfiling, we have developed an automated patient recognition system for chest CT images. The image database consisted of 100 cases with present and previous chest CT images. A volume of interest (VOI) measuring 40 × 40 pixels was selected from the left lung region, bronchus region, and right lung region. Next, the overall lung region and these three regions in a current chest CT image were used as a template for determining the residual value with the corresponding four regions in previous chest CT images. To ensure separation between the same and different patients, we applied a combined analysis that employed the ruled-based plus artificial neural network (ANN) method. The overall performance of the method developed was examined in terms of receiver operating characteristic (ROC) curves. The performance of the rule-based plus ANN method using a combination of the four regions was higher than obtained using a rule-based method using these four regions separately. The automated patient recognition system using the rule-based plus ANN method achieved an area under the curve (AUC) value of 0.987. This automated patient recognition method for chest CT images is promising for helping to retrieve misfiled patient images, especially in a PACS environment.