To overcome the weak edges and large noise of flotation froth image, and to solve the weakness of traditional valley detection algorithm on different kinds of bubble segmentation sizes, a froth image segmentation method was proposed based on Contourlet transform multi-scale edge enhancement and adaptive valley detection. Firstly, the froth image was decomposed by using the Contourlet transfom to obtain multi-scale and multi-direction sub-band coefficients. Then, thresholds of the nonlinear enhancement function were determined according to the coefficients of each scale to enhance edges and suppress the noise. Furthermore, the optimal position adjustment strategy and parameter setting of HS were improved to find the optimal parameters of valley detection algorithm and to detect the different kinds edges of bubble image size. Finally, segmentation experiment was performed and obtained result was further improved by morphological processing. Experiments show that the proposed method effectively detects the edges of different type of bubbles adaptively, and the average detection efficiency (DER) is 91.2% and the average accuracy (ACR) is 90.6%, which is much better than that of traditional methods. This method has high precision, good adaptive ability, and does not need to adjust parameters manually.