A New Image Thresholding and Gradient Optimization Algorithm Using Object Class Uncertainty Theory

摘要

The knowledge of thresholding and gradients at different object interfaces is of paramount interest for image segmentation and other imaging applications. Most thresholding and gradient optimization methods primarily focus on image histograms and therefore, fail to harness the information embedded in image intensity patterns. Here, we investigate the role of a recently conceived object class uncertainty theory in image thresholding and gradient optimization. The notion of object class uncertainty, a histogram-based feature, is formulated and a computational solution is presented. An energy function is designed that captures spatio-temporal correlation between class uncertainty and image gradient which forms objects and shapes. Optimum thresholds and gradients for different object interfaces are determined from the shape of this energy function. The underlying theory behind the method is that objects manifest themselves with fuzzy boundaries in an acquired image and, in a probabilistic sense, intensities with high class uncertainty are associated with high image gradients generally appearing at object interfaces. The method has been applied on several medical as well as natural images and both thresholds and gradients have successfully been determined for different object interfaces even when some of the thresholds are almost impossible to locate in respective histograms.