A new wavelet-based automatic multi-level thresholding technique is proposed. The new technique is a generalized version of the method proposed by Olive . Olive  proposed using a set of dilated wavelets to convolve with the histogram of an image. For each scale, a set of thresholds was determined automatically based on the rules he proposed. However, Olive did not provide a systematic way to decide on an exact set of thresholds which corresponds to a specific scale that can lead to the best segmentation result. In this paper, we propose using a cost function as a guide to solve the above problem. Experimental results show that our approach can always automatically select the best scale for performance of multi-level thresholding.