For image denoising by wavelet transforms, it is very important how we decide the threshold. The “universal threshold” proposed by Donoho gives the optimal threshold theoretically, but the information of noise power on the image is needed. In this study, we used the information of the histogram shape of wavelet coefficients to choose optimal threshold. We selected a medical chest x-ray image of various signal-to-noise ratios for wavelet denoising. We found that our method was effective when there were many noises on the image.