An active contour model based on shadow image and reflection edge for image segmentation

Abstract

Image segmentation is popular in many applications. Active contour models (ACMs) are very useful methods for image segmentation. However, many existing ACMs have drawbacks, e.g., obtaining poor performance for segmenting images with intensity inhomogeneity, or excessive convolution operations increasing calculation time. To solve these problems, a novel ACM based on shadow image and reflection edge (SIRE) is proposed, which represents the image by an additive model with the shadow image and the reflection edge. The shadow image is calculated with mean filtering, and the reflection edge is calculated by the optimal solution of the data driven term within the energy function. The image energy function is minimized by the level set method (LSM), by which the image segmentation is realized. The difference between the background and the target is adequately reflected by the reflection edge, which drives the evolution of the contour lines to find the target edge correctly. In the level set calculation, the optimized length term and the distance regularization term are used to improve the model robustness. Experimental results demonstrate that the proposed method can effectively segment inhomogeneous images, and that our model outperforms other three ACMs in terms of segmentation speed and accuracy.