Retrospective correction of near field effect of X-ray source in radiographic images by using genetic algorithms

X-ray bone images are used in the areas such as bone age assessment, bone mass assessment and examination of bone fractures. Medical image analysis is a very challenging problem due to large variability in topologies, medical structure complexities and poor image modalities such as noise, low contrast, several kinds of artifacts and restrictive scanning methods. Computer aided analysis leads to operator independent, subjective and fast results. In this study, near field effect of X-ray source is eliminated from hand radiographic images. Firstly, near field effect of X-ray source is modeled, then the parameters of the model are estimated by using genetic algorithms. Near field effect is corrected for all image pixels retrospectively. Two different categories of images are analyzed to show the performance of the developed algorithm. These are original X-ray hand images and phantom hand images. Phantom hand images are used to analyze the effect of noise. Two performance criteria are proposed to test the developed algorithm: Hand segmentation performance and variance value of the pixels in the background. It is observed that the variance value of the pixels in the background decreases, and hand segmentation performance increases after retrospective correction process is applied.