The present invention discloses a multi-scale grain image segmentation based on self-adaptive window fixation and spread. The process comprises the steps as follows: an image block n corresponding tothe grain of an image to be segmented is picked up for wavelet transform, and a corresponding HMT model parameter theta n thereof is determined; the corresponding likelihood value of corresponding data block at each wavelet decomposition scale of the image to be segmented and the corresponding likelihood value of the pixel of the image to be segmented are determined respectively and are combined together to find out the likelihood value n required by finial fusion; the likelihood value on fusion widest scale (k is 4) is found out, and the corresponding segmentation result plotting on the scale is determined also; a marking field on the fusion scale k and the physical clustering center of each grain are confirmed; the multi-scale segmentation of the self-adaptive window fixation and spread is used for find out the segmentation result plotting on next fusion scale k minus 4; the finial segmentation result is confirmed by judging whether the fusion scale of the segmentation result plotting is zero or not. The multi-scale grain image segmentation has the advantages of area consistency and good edge positioning performance and can be used for image segmentation comprising grain information.