/
apply_intensity_transformation.m
74 lines (59 loc) · 2.9 KB
/
apply_intensity_transformation.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
function apply_intensity_transformation(input_path, output_path, m_k, methodT)
% **************************************************************************************************
% Intensity normalization of MRI scans. Function to apply the
% learned intensity landmarks (m_k) into the input image.
%
% Normalization method based on Nyul et al 2000
%
% - L. G. Nyul, J. K. Udupa, and X. Zhang, “New variants of a
% method of MRI scale standardization,” IEEE Trans. Med. Imaging, no. 2, pp. 143–150, 2000.
%
% - M. Shah, Y. Xiao, N. Subbanna, S. Francis, D. L. Arnold, D. L.
% Collins, and T. Arbel, “Evaluating intensity normalization of
% MRIs of human brain with multiple sclerosis,” Med. Image Anal., vol. 15, no. 2, pp. 267–282, 2011.
%
% svalverde@eia.udg.edu 2016
% NeuroImage Computing Group. Vision and Robotics Insititute (University of Girona)
%
% **************************************************************************************************
% load the input image
im_path = (input_path);
current_scan = load_nifti(im_path);
current_image = double(current_scan.img);
template_brainmask = current_image > 0.05;
template = current_image(template_brainmask == 1);
% force the same data type
m_k.landmarks = double(m_k.landmarks);
% find the minimum and maximum percentiles (p1 and p99) and the deciles (p10...p90)
Y = sort(template(:));
minT=min(Y);
T.values(1) = Y(ceil(0.01.*length(Y)));
T.values(2) = Y(ceil(0.1.*length(Y)));
T.values(3) = Y(ceil(0.2.*length(Y)));
T.values(4) = Y(ceil(0.3.*length(Y)));
T.values(5) = Y(ceil(0.4.*length(Y)));
T.values(6) = Y(ceil(0.5.*length(Y)));
T.values(7) = Y(ceil(0.6.*length(Y)));
T.values(8) = Y(ceil(0.7.*length(Y)));
T.values(9) = Y(ceil(0.8.*length(Y)));
T.values(10) = Y(ceil(0.9.*length(Y)));
T.values(11) = Y(ceil(0.99.*length(Y)));
maxT=max(Y);
%set values before P1 and after P99 to zero
mask=(current_image<T.values(1));
SscaleExtremeMin= m_k.landmarks(1) + ( minT - T.values(1) ) / ( T.values(2)-T.values(1) ) * (m_k.landmarks(2) - m_k.landmarks(1));
SscaleExtremeMax= m_k.landmarks(10)+ ( maxT - T.values(10) ) / ( T.values(11)-T.values(10) ) * (m_k.landmarks(11) - m_k.landmarks(10));
if strcmp(methodT,'linear')
%normalized_scan = interp1( [minT T.values maxT], [SscaleExtremeMin m_k.landmarks SscaleExtremeMax], current_image) ;
normalized_scan = interp1( T.values, m_k.landmarks, current_image) ;
elseif strcmp(methodT,'spline')
%normalized_scan = spline( [minT T.values maxT], [SscaleExtremeMin m_k.landmarks SscaleExtremeMax], current_image);
normalized_scan = spline( T.values, m_k.landmarks, current_image);
else
error('Invalid value for Method')
end
normalized_scan(mask)=0;
% save the normalized scan
current_scan.img = normalized_scan;
save_nifti(current_scan, output_path,'c');
end