An Adaptive Fuzzy Level Set Model with Local Spatial Information for Medical Image Segmentation and Bias Correction

Abstract

Medical image segmentation is still a challenging task due to noise and intensity inhomogeneity. An adaptive fuzzy level set model (AFLSM) with local spatial information is presented in this paper for accurately segmenting medical images and correcting bias field. A weighting scheme that can ensure each pixel in the neighborhood to have anisotropic weight is first introduced to remove noisy pixels and hence improve the robustness to noise. Then, a linear combination of orthogonal basis functions is used to represent bias field to ensure its smoothly and slowly varying property. Besides, to improve the robustness to initialization, this adaptive fuzzy level set model fuses a level set model with the membership function of fuzzy clustering, which can adaptively adjust the evolution of level set function. Finally, the distance regularization term in energy formulation is redefined with a novel double-well potential function to inherently maintain the accuracy and stability of the AFLSM. The AFLSM is first represented in the two-phase case and subsequently extended to the multi-phase formulation. The numerous visual segmentation results and quantitative evaluation can demonstrate the performance of the AFLSM on synthetic and real medical images. Comparison with the state-of-the-art models shows that the AFLSM can achieve better segmentation results with an improvement of 0.2286 ± 0.1477 in Dice coefficient and 0.1350 ± 0.0661 in Jaccard similarity coefficient in terms of robustness and the capability to correct bias field, respectively.