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.