A novel level set method for image segmentation by combining local and global information

Abstract

A novel level set method integrating local and global statistical information is proposed in this paper. In our method, a new signed pressure force (SPF) function is constructed by two parts. One is the global average intensity of the image, which can accelerate the evolution of the curve when the contour far away from the object boundaries. The other is the intensity average of difference image between the averaging convolution image and the original image, which can guide the evolving curve to catch the boundaries of the objects. In addition, an adaptive weighting function is utilized to adjust the ratio between the global and local terms, which can eliminate the inconvenient selection of weighting parameter. By substituting the new SPF function for the edge stopping function of the geodesic active contour model, we obtain a novel adaptive hybrid segmentation model, which is capable of segmenting the images with intensity inhomogeneity. What is more, in our method, the level set function is initialized with a binary function, which reduces the computational cost for the re-initialization step. The experimental results and comparisons with several popular models on synthetic and real images indicate that our method achieves superior performance in segmenting images with noise, low contrast and intensity inhomogeneity.

Keywords:

• Image segmentation
• signed pressure force function
• level set method
• local image information
• global image information

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was jointly supported by the 111 Project of Chinese Ministry of Education [grant number B12018]; the National Natural Science Foundation of China [grant number 61373055], [grant number 61672265].