Code covered by the BSD License

# Reinhard Stain Normalization

by

### Manohar (view profile)

This approach maps the colour distribution of an over/under stained image to that of a well stained

RGB2Lab(R,G,B)
```function [L,a,b] = RGB2Lab(R,G,B)
%RGB2LAB Convert an image from RGB to CIELAB
%
% function [L, a, b] = RGB2Lab(R, G, B)
% function [L, a, b] = RGB2Lab(I)
% function I = RGB2Lab(...)
%
% RGB2Lab takes red, green, and blue matrices, or a single M x N x 3 image,
% and returns an image in the CIELAB color space.  RGB values can be
% either between 0 and 1 or between 0 and 255.  Values for L are in the
% range [0,100] while a and b are roughly in the range [-110,110].  The
% output is of type double.
%
% This transform is based on ITU-R Recommendation BT.709 using the D65
% white point reference. The error in transforming RGB -> Lab -> RGB is
% approximately 10^-5.
%

% By Mark Ruzon from C code by Yossi Rubner, 23 September 1997.
% Updated for MATLAB 5 28 January 1998.
% Updated for MATLAB 7 30 March 2009.

if nargin == 1
B = double(R(:,:,3));
G = double(R(:,:,2));
R = double(R(:,:,1));
end

if max(max(R)) > 1.0 || max(max(G)) > 1.0 || max(max(B)) > 1.0
R = double(R) / 255;
G = double(G) / 255;
B = double(B) / 255;
end

% Set a threshold
T = 0.008856;

[M, N] = size(R);
s = M * N;
RGB = [reshape(R,1,s); reshape(G,1,s); reshape(B,1,s)];

% RGB to XYZ
MAT = [0.412453 0.357580 0.180423;
0.212671 0.715160 0.072169;
0.019334 0.119193 0.950227];
XYZ = MAT * RGB;

% Normalize for D65 white point
X = XYZ(1,:) / 0.950456;
Y = XYZ(2,:);
Z = XYZ(3,:) / 1.088754;

XT = X > T;
YT = Y > T;
ZT = Z > T;

Y3 = Y.^(1/3);

fX = XT .* X.^(1/3) + (~XT) .* (7.787 .* X + 16/116);
fY = YT .* Y3 + (~YT) .* (7.787 .* Y + 16/116);
fZ = ZT .* Z.^(1/3) + (~ZT) .* (7.787 .* Z + 16/116);

L = reshape(YT .* (116 * Y3 - 16.0) + (~YT) .* (903.3 * Y), M, N);
a = reshape(500 * (fX - fY), M, N);
b = reshape(200 * (fY - fZ), M, N);

if nargout < 2
L = cat(3,L,a,b);
end
```