AUTOMATED SEGMENTATION OF ANATOMIC REGIONS IN CHEST RADIOGRAPHS USINGAN ADAPTIVE-SIZED HYBRID NEURAL-NETWORK

The purposes of this research are to investigate the effectiveness of our novel image features for segmentation of anatomic regions such as the lungs and the mediastinum in chest radiographs and to develop an a utomatic computerized method for image processing. A total of 85 scree ning chest radiographs from Johns Hopkins University Hospital were dig itized to 2 K by 2.5 K pixels with 12 bit gray scale. To reduce the am ount of information, the images were smoothed and subsampled to 256 by 310 pixels with 8 bit. The determination approach consists of classif ying each pixel into two anatomic classes (lung and others) on the bas is of several image features: (1) relative pixel address (Rx,Ry) based on lung edges extracted through image processing using profile, (2) d ensity normalized from lungs and mediastinum density, and (3) histogra m equalized entropy. The combinations of image features were evaluated using an adaptive-sized hybrid neural network consisting of an input, a hidden, and an output layer. Fourteen images were used for the trai ning of the neural network and the remaining 71 images for testing. Us ing four features of relative address (Rx,Ry), normalized density, and histogram equalized entropy, the neural networks classified lungs at 92% accuracy against test images following the same rules as for the t raining images. (C) 1998 American Association of Physicists in Medicin e. [S0094-2405 (98)02906-X].