Region segmentation using information divergence measures

Image segmentations based on maximum likelihood or maximum a posteriori analyses of object textures usually assume parametric models (e.g., Gaussian) for distributions of these features. For real images, parameter accuracy and model stationarity may be elusive, so that model-free inference methods ought to have an advantage over those that are model-dependent. Functions of the relative entropy (RE) from information theory can produce minimum error, model-free inferences, and can detect the boundary of an image object by maximizing the RE between the pixel distributions inside and outside a flexible curve contour. A generalization of the RE – the Jensen–Rényi divergence (JRD) – computes optimal n-way decisions and can contour multiple objects in an image simultaneously. Seed regions expand naturally and multiple contours tend not to overlap. An edge detector based on the JRD, combined with multivariate pixel segmentation, generally improved the error of the segmentation. We apply these functions to contour patient anatomy in X-ray computed tomography for radiotherapy treatment planning.