The Gabor-Based Tensor Level Set Method for Multiregional Image Segmentation

This paper represents a new level set method for multiregional image segmentation. It employs the Gabor filter bank to extract local geometrical features and builds the pixel tensor representation whose dimensionality is reduced by using the offline tensor analysis. Then multiphase level set functions are evolved in the tensor field to detect the boundaries of the corresponding image. The proposed method has three main advantages as follows. Firstly, employing the Gabor filter bank, the model is more robust against the salt-and-pepper noise. Secondly, the pixel tensor representation comprehensively depicts the information of pixels, which results in a better performance on the non-homogenous image segmentation. Thirdly, the model provides a uniform equation for multiphase level set functions to make it more practical. We apply the proposed method to synthetic and medical images respectively, and the results indicate that the proposed method is superior to the typical region-based level set method.