Automatic and exam-type independent algorithm for the segmentation and extraction of foreground, background, and anatomy regions in digital radiographic images

Processing optimization of digital radiographs requires the knowledge of the location and characteristics of both diagnostically relevant and irrelevant image regions. An algorithm has been developed that can automatically detect and extract foreground, background, and anatomy regions from a digital radiograph. This algorithm is independent of exam-type information and can deal with multiple-exposed computed radiography (CR) images. First, the image is subsampled, and the processing is done on the sub-sampled image to improve subsequent processing efficiency and reduce algorithm dependency on image noise and detector characteristics. Second, an initial background is detected using adaptive thresholding on the cumulative histogram of significant transition pixels and an iterative process, based on background variance. Third, foreground detection is conducted by: (1) classifying all significant transitions using a smart-edge detection, (2) delineating all lines that are possible collimation blades using Hough transform, (3) finding candidate partition blade pairs if the image has several radiation fields, (4) partitioning the image into sub-images containing only one radiation field using a divide-and-conquer process, and (5) identifying the best collimation for each sub-image from a tree-structured hypothesis list. Fourth, the background is regenerated using a region-growing process from identified background “seeds.” Fifth, the background and foreground regions are merged and removed; the rest of the image is labeled and those large, connected regions are identified as anatomy regions. The algorithm has been trained and tested separately with two image sets from a wide variety of exam types. Each set consists of more than 2700 CR images acquired with KODAK DIRECTVIEW CR 800 Systems. The overall success rate in detecting both foreground and background is 97%.