A new lattice Boltzmann algorithm for assembling local statistical information with MR brain imaging segmentation applications

Abstract

Recently, the lattice Boltzmann (LB) model has been used in medical imaging segmentation as alternatives for level set methods. The advantages of the LB model include its simple programming and the fact that it is easily paralleled, which can shorten computing times during real-time processing. However, traditional LB algorithms often fail to segment magnetic resonance (MR) brain images, which usually contain low-contrast intensity levels, noise and bias field. To solve these problems, this paper proposes a new LB algorithm assembled by local statistical region information, which can increase the between-class variances of the foreground and background by reducing intra-class variations and can achieve a better anti-noise performance by smoothing the noise of neighborhood pixels. To test its effectiveness and efficiency, comparison experiments were carried out with other LB and non-LB algorithms. The results show that our algorithm was validated on synthetic images and real MR images with desirable performance in the presence of low-contrast gray levels and noise. It also achieved best segmentation performance (with Dice coefficient 97.9 %) compared to other algorithms (with Dice coefficient 80.33, 58.11, 88.2, 81.77, 96.1 % respectively). In addition, the computing speed of the new algorithm is acceptable (18.65–27.62 s).