Adaptive tuning noise estimation for medical images using maximum element convolution Laplacian

Noise in medical images can adversely affect the outcome of clinical diagnosis. In analyzing medical images, noise estimation is necessary to ensure consistency and performance quality of image processing techniques. In this study, we present a noise estimation method, namely Adaptive Tuning Noise Estimation (ATNE) that implements convolution Laplacian noise estimation. ATNE is based on subtraction of Gabor wavelet detected edges of images, and involves the relation element based on the parameters of the input image. This method allows a fast estimation of the image noise variance without a heavy computational cost. To assess the effectiveness of ATNE, 1000 mammograms are used. We pre-process these images to be Rician distributed with various noise variances. ATNE is used to estimate the noise level of the resulting images. We compare ATNE with other noise estimation methods, and the results show that ATNE outperforms other related methods with a lower percentage of error for noise variance estimation.