Image segmentation based on the local minium cross-entropy and quad-tree

With maximum entropy principle, satisfactory segmentation can be attained in dealing with the various sizes of objects. However, for some inhomogeneous images, due to the factors of inhomogeneous illumination, the global threshold cannot be used to segment all objects. On the basis of the current threshold algorithms and with the deduction of the relationships between entropy of the original set and ones of subsets, this article develops an image segmentation method based on local minimum cross-entropy, so to meet the requirements of inhomogeneous cell images. Moreover, the article presents a realization process of the algorithm that is combined with the quad-tree model, which has the advantageous of less computation and better segmentation effect, in comparison with other algorithms of adaptive threshold method.