Adaptive active contours driven by the squared Hellinger distance and local correlation features for inhomogeneous image segmentation

Intensity inhomogeneity is an intractable issue that leads to significant challenges for image segmentation. Moreover, inhomogeneous images are very common in real life, especially the medical images generated by medical instruments. Most existing active contour models yield poor performance when they are applied to segment these images. Thus, a new image segmentation model is developed in this paper by integrating the squared Hellinger distance and multiple truncation functions that are defined by local correlation features into the level set framework. As a result, the problems of intensity inhomogeneity and dark edges can be effectively solved. Specifically, the Hellinger distance is first defined as a fitting method of the energy function and has better performance in splitting the boundary of dark areas in inhomogeneous images. More importantly, the proposed model constructs local correlation features (LCFs) to redefine the scale of the local areas and makes the division of the local information more reasonable, which effectively enhances the accuracy of inhomogeneous images. Then, to combine the global and local information of the image, an adaptive combination coefficient function defined by the evolution process is applied in our model; moreover, the length term combined with the saliency feature of the image achieves a remarkable improvement effect. Finally, all terms are integrated into a variational level set framework, and our new image segmentation model is proposed. The experimental results on medical and natural inhomogeneous images demonstrate the excellent performance of our model over most state-of-the-art active contour models in terms of accuracy and robustness.