An improved method for automatic identification of lung regions in chest radiographs

An algorithm is developed for fast, accurate identification of lung fields in chest radiographs for use in various computer-aided diagnosis (CAD) schemes. The method we presented simplifies the current approach of edge detection from derivatives by using only the first derivative of the profiles of each image, and combining it with pattern classification and image feature analysis in determining both the region of interest (ROI) and the actual lung boundaries. Moreover, instead of using the traditional curve fitting to delineate the detected lung field, we applied an iterative contour smoothing algorithm to each of the four detected boundary segments (lateral, medial, top and diaphragm edges) to form a closed smooth boundary for each lung. These improvements result in dramatic reduction of the running time and more accurate boundary detection, especially the diaphragm edges. The proposed algorithm has been tested with 40 posterior-anterior (PA) chest images. The detected left and right lung fields have an averaged accuracy of over 95%.