Abstract:The narrow-band method is a common acceleration method for level set image segmentation. The traditional narrow-band still has redundant computational regions; When the traditional narrow-band method is combined with the LATE (Local Approximation of Taylor Expansion) level set model, the image segmentation efficiency may be reduced. In order to solve these problems, a rectangular narrow-band method based on LATE level set image segmentation model is proposed in this study. The level set is subjected to the following narrow-band processing before each LATE level set iteration. First, find out all the points of zero crossings of the level set; second constrict the points of zero crossings by the activity constraints, eliminate the inactive points of zero crossings, and effectively reduce the area of the narrow-band, then generate a rectangular narrow-band for the points of zero crossings by the active constraints, optimize the overlapping rectangular narrow-band so that the total area of the rectangular narrow-band is as small as possible. Finally, the level set differential equation is solved in the narrow-band of the rectangle, and the level set is updated to complete this iteration. In the different stages of the level set evolution, the area of the traditional narrow-band and the rectangular narrow-band of this study are compared. As the number of iterations increases, the ratio of the area of rectangular narrow-band to the area of traditional narrow-band is gradually reduced to zero, indicating that the rectangular narrow-band method effectively reduces the amount of redundancy calculation. For images with different degrees of intensity inhomogeneity, the proposed method is compared with the LATE method, the direct narrow-band method, and the DTM narrow-band method. The direct narrow-band method and the DTM narrow-band method have lower segmentation efficiency than the LATE method, and the segmentation quality is greatly affected for some images with severe intensity inhomogeneity. Under the condition of maintaining good segmentation effect, the segmentation speed of the proposed method is faster than that of LATE method. The rectangular narrow-band method in this study effectively reduces the complexity of the algorithm and improves the efficiency of image segmentation.