阈值的选择是图像阈值分割法的关键，针对现有阈值法中存在的没有充分地考虑图像像素之间的空间相关信息等问题，提出把Hilbert图像扫描方法和小波变换相结合，获得了连续光滑的阈值曲线，从而建立了一种局部自适应阈值法。首先通过Hilbert图像扫描的方法将2维图像信息转化为1维Hilbert序列；然后利用小波变换对其进行多分辨分析获得信号的发展趋势曲线，并将该曲线作为阈值曲线对Hilbert序列进行量化处理；最后对量化后的Hilbert序列运用Hilbert图像扫描的反过程恢复为2维图像信息，从而实现原图像的分割。该方法所建立的阈值曲线能够随像素点的环境变化而自适应调整，反映出当前区域图像灰度信息的变化趋势，从而充分地保留了图像的局部信息和原图像中相邻像素的相关性，提高了图像分割效率。实验结果表明，该方法具有分割性能好以及受噪声影响小等优点，是一种非常有效的图像分割方法。

Threshold selection is the critical process of image threshold segmentation method.Considering the neglect of spatial correlation of image pixels in current threshold segmentation methods,we propose to combine Hilbert image scan with wavelet transform to obtain a continuous and smooth threshold curve,and then propose a local self adaptive threshold method in this paper.Firstly,the image is translated into 1D Hilbert order via Hilbert image scan method；Secondly,the curve of the developing trend of the Hilbert order is obtained by the multi resolution analysis using wavelet transform.Furthermore this curve is chosen as self adaptive threshold and the Hilbert order binarization is realized.Lastly,the binaried Hilbert order is translated into 2D image using the reverse Hilbert matrix scan and the image segmentation is achieved.The threshold curve achieved by the above mentioned method is able to self adaptively adjust along with neighborhood property,and reflects the developing trend of grayscale information in current image region.So the present algorithm preserves the local information and the relativity of adjacency pixels in the image.Moreover,it also improves the efficiency of image segmentation.Experiments indicate that the proposed method is an extraordinary effective image segmentation technique.It is with very good performance and immunes to noise.