A computer-aided diagnosis scheme for the detection of interstitial disease in standard digital posteroanterior (PA) chest radiographs is presented. The detection technique is supervised-manually labelled data should be provided for training the algorithm-and fully automatic, and can be used as part of a computerized analysis
scheme for X-ray lung images.
Prior to the detection, a segmentation should be performed which delineates the lung field boundaries.
Subsequently, a quadratic decision rule is employed for every pixel within the lung fields to associate with each pixel a probabilistic measure indicating interstitial disease. The locally obtained per-pixel probabilities are fused to a single global probability indicating to what extent there is interstitial disease present in the image. Finally, a threshold on this quantity classifies the image as containing interstitial disease or not.
The probability combination scheme presented utilizes the quantiles of the local posterior probabilities to fuse the local probability into a global one. Using this nonparametric technique, reasonable results are obtained on the interstitial disease detection task. The area under the receiver operating characteristic equals 0.92 for the
optimal setting.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.