RT Conference Proceedings
T1 Rician noise estimation for 3D Magnetic Resonance Images based on Benford's Law.
A1 Maza Quiroga, Rosa María
A1 Thurnhofer-Hemsi, Karl
A1 López-Rodríguez, Domingo
A1 López-Rubio, Ezequiel
K1 Imágenes por resonancia magnética - Ruido
AB In this paper, a novel method to estimate the level of Rician noise in magnetic resonance images is presented. We hypothesize that noiseless images follow Benford's law, that is, the probability distribution of the  first digit of the image values is logarithmic. We show that this is true when we consider the raw acquired image in the frequency domain. Two measures are then used to quantify the (dis)similarity between the actual distribution of the fi rst digits and the more theoretical Benford's law: the Bhattacharyya coefficient and the Kullback-Leibler divergence. By means of these measures, we show that the amount of noise directly affects the distribution of the fi rst digits, thereby making it deviate from Benford's law. In addition, in this work, these  findings are used to design a method to estimate the amount of Rician noise in an image. The utilization of supervised machine learning techniques (linear regression, polynomial regression, and random forest) allows predicting the parameters of the Rician noise distribution using the dissimilarity between the measured distribution and Benford's law as the input variable for the regression. In our experiments, testing over magnetic resonance images of 75 individuals from four different repositories, we empirically show that these techniques are able to precisely estimate the noise level present in the test T1 images.
PB Springer Nature
YR 2021
FD 2021
LK https://hdl.handle.net/10630/31428
UL https://hdl.handle.net/10630/31428
LA eng
NO Maza-Quiroga, R., Thurnhofer-Hemsi, K., López-Rodríguez, D., López-Rubio, E. (2021). Rician Noise Estimation for 3D Magnetic Resonance Images Based on Benford’s Law. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12906. Springer, Cham. https://doi.org/10.1007/978-3-030-87231-1_33
NO Política de acceso abierto tomada de: https://www.springernature.com/gp/open-research/policies/book-policies
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026