www.gusucode.com > 瑞利信道下噪声能量的估计源码程序 > 瑞利信道下噪声能量的估计源码程序/MSP_estimate_version1/ReferenceMethods/Brummer.m
% Brummer's method % M.E. Brummer, et al., Automatic detection of brain contours in MRI data sets, IEEE Transactions on Medical Imaging, vol. 12.2 (1993): 153-166 % % 30/12/2013 % % Tomasz Pieciak % AGH university of Science and Technology, Krakow, Poland % pieciak@agh.edu.pl, http://home.agh.edu.pl/pieciak/ % % ARGUMENTS % data - single-coil MRI data % bins - histogram bins % % FUNCTION RETURNS % sigma - estimated noise level (sigma) % % USAGE % sigma_estimated = Brummer(data, 1000) function [sigma] = Brummer(data, bins) % initial noise level searching [histogram_p, histogram_x] = hist(data(:), bins); histogram_p_filtered = filtfilt(ones(1,25), 1, histogram_p); % low-pass filter (window 1x25) [value, index] = max(histogram_p_filtered); fc = histogram_x(2*index); % minimization procedure - least-squares fitting procedure [h, f] = hist(data(data <= fc), bins); % letters according to eq. 12 in 'Automatic detection of brain contours in MRI data sets', p. 156 F_MIN = @(x) sum((h - x(2) .* (f./x(1).^2) .* exp(-(f.^2)./(2.*x(1).^2))).^2); % according to eq. 12, p.156 % x(1) - sigma, x(2) - K sigma0 = histogram_x(index); K0 = 1; sigma_K = fminsearch(F_MIN, [sigma0, K0]); sigma = sigma_K(1);