Implicit Active Contours Driven by Local and Global Image Fitting Energy for Image Segmentation and Target Localization

We propose a novel active contour model in a variational level set formulation for image segmentation and target localization. We combine a local image fitting term and a global image fitting term to drive the contour evolution. Our model can efficiently segment the images with intensity inhomogeneity with the contour starting anywhere in the image. In its numerical implementation, an efficient numerical schema is used to ensure sufficient numerical accuracy. We validated its effectiveness in numerous synthetic images and real images, and the promising experimental results show its advantages in terms of accuracy, efficiency, and robustness. 1. Introduction Image segmentation is one of the most important operations in the fields of image processing and computer vision. In the past two decades, image segmentation techniques have been widely studied and various novel methods have been proposed; especially the active contour models [1], which are based on the theory of surface evolution and geometric flows, have many successful and promising applications. The basic idea of the active contour models is to define dynamic curves which are evolved in an image domain under the constraints of internal forces and external forces. Generally, the internal forces are derived from the curve itself and the external forces are generated from the image. Active contour models are originally presented in terms of a parametric contour with some drawbacks associated with the difficulty in coping with the complex topological changes and poor dependency of parameterization. Later the level set [2] method was introduced to represent the contour, which improves the active contour models to be completely free of these drawbacks. In the level set method, the contour is represented as the zero level set of a higher dimensional function which is usually called a level set function, and hence the motion of the contour can be implicitly shown by the evolution of the level set function. Motivated by the level set method, a large number of researchers focused on the study of the implicit active contour, including the definition of the image-based driven force, the adaptation to different applications, and the numerical stability. According to the difference of image-based nature constraints, the existing active contours models can be categorized into two types: edge-based models [3–7] and region-based models [8–13]. The edge-based models, which typically employ image gradient to construct an edge detector to attract the contours toward the desired boundaries of the objects and finally