A basic study of the false positive candidate reduction technique for the computer aided diagnosis of a chest radiograph

In recent years, a computer performs analysis of medical picture information and the scheme of computer-aided diagnosis (CAD) which support doctor\u27s diagnosis attracts attention. Chest radiographs can be taken by the comparatively easy method, and are used for diagnosis of the chest disease in a mass screening. Consequently, since the numbers of radiograph become extensive, interpretation impose a heavy burden for doctors. Then, the research of the scheme of computer-aided diagnosis that tells the position of the nodule in a radiograph is advanced, in order to mitigate a doctor\u27s burden. However, since there are many false positive candidates that tell as a candidate what is not the pulmonary nodule accidentally, it is not practical. In this research, we present the false positive candidate reduction technique in the scheme of computer-aided diagnosis intended for pulmonary nodule of chest radiograph

A basic study of computer-aided diagnosis system for interstitial pneumonia by chest X-ray image

In this study, we suggest that the method using second-order statistics for distinguishing normal images and interstitial pneumonia images. This method was composed of automatic extraction of lung region, setup of ROIs (Region of interest) to intercostals space, discrimination of ROIs using second-order statistics. The second order statistics used this method is co-occurrence matrix and run-length matrix and the features obtained these matrices can quantify microscopic variation of density. At least, our method was estimated by calculating the ratio of abnormal ROIs. Consequently, we could obtain a relatively good result. From these things, we suggested that this method be useful to the discrimination between interstitial pneumonia images and normal images

An Experiment to Estimate Efficiency of Java Applet on WWW

投稿論