Level set method for image segmentation based on local variance and improved intensity inhomogeneity model. Issue 12 (1st December 2016) - Li, Zhongguo; Zeng, Lei; Xu, Yifu; Chen, Jian; Yan, Bin
Abstract : This study proposes an improved level set method for segmenting images with intensity inhomogeneity. One of the improvements is to consider the difference between an original image and an estimated image without bias field in the image model. Apart from using this difference, Gaussian distribution with means and variance is utilised as the local intensity descriptor to map the original image into another domain so the object and the background can be better separated in the transformed domain. Then, an improved level set energy function that combines the image term, local variance, and the above difference is defined. The minimisation of the function can be processed by level set evolution. The proposed method is compared with existing methods, and experiments on both synthetic and real images demonstrate that authors' method has superior performance.