Since segmented noisy image using multiscale markov random fields(MRF) still has very small error blocks or single error pixel in the smooth area

this paper proposes an approach associate edge information with multiscale MRF to reduce error classification.Because images are corrupted by the white gaussian noise

edge is difficult to detect successfully.To detect edge

an algorithm based on the stationary wavelet transform and multiplication between neighbour scales is designed. When there is a significant feature at some position

the wavelet coefficients at that position across scales have high correlation.Such correlation can be taken by multiplication between scales

so edge can be located better.Analysis and experiments demonstrate that the proposed edge detection algorithm is effective and fast.Better results are obtained using the segmentation algorithm associate with edge

most error classification among the smooth image area is corrected.The amount of added computation is only brought by the expense of the edge detection procedure.These new edge detection and segmentation algorithm are more fit for strong noisy images.