Initial commit

README.md

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-# staining-normalization
-Staining unmixing and normalization for histopathology images
+# Staining unmixing and normalization
+
+NOTE: Originally published at the Assessment of Mitosis Detection Algorithms 2013 challenge website (http://http://amida13.isi.uu.nl).
+
+One of the major difficulties in histopathology image analysis is apperance variability. For example, when performing mitosis detection, many false positives can arise when the histopathology slide is overstained. This MATLAB code performs staining unmixing (separation of the hematoxylin and eosing stains) and apperance normalization. It is based on the method described in [1]. Some examples of staining normalization can be seen in the figure below.
+
+[1] A method for normalizing histology slides for quantitative analysis, M Macenko, M Niethammer, JS Marron, D Borland, JT Woosley, G Xiaojun, C Schmitt, NE Thomas, IEEE ISBI, 2009. dx.doi.org/10.1109/ISBI.2009.5193250
+

licence.txt

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+Copyright (c) 2013, Mitko Veta
+Image Sciences Institute
+University Medical Center
+Utrecht, The Netherlands
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+

normalizeStaining.m

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+function [Inorm, H, E] = normalizeStaining(I, Io, beta, alpha, HERef, maxCRef)
+% Normalize the staining appearance of images originating from H&E stained
+% sections.
+%
+% Example use:
+% See test.m.
+%
+% Input:
+% I         - RGB input image;
+% Io        - (optional) transmitted light intensity (default: 240);
+% beta      - (optional) OD threshold for transparent pixels (default: 0.15);
+% alpha     - (optional) tolerance for the pseudo-min and pseudo-max (default: 1);
+% HERef     - (optional) reference H&E OD matrix (default value is defined);
+% maxCRef   - (optional) reference maximum stain concentrations for H&E (default value is defined);
+%
+% Output:
+% Inorm     - normalized image;
+% H         - (optional) hematoxylin image;
+% E         - (optional)eosin image;
+%
+% References:
+% A method for normalizing histology slides for quantitative analysis. M.
+% Macenko et al., ISBI 2009
+%
+% Efficient nucleus detector in histopathology images. J.P. Vink et al., J
+% Microscopy, 2013
+%
+% Copyright (c) 2013, Mitko Veta
+% Image Sciences Institute
+% University Medical Center
+% Utrecht, The Netherlands
+% 
+% See the license.txt file for copying permission.
+%
+
+% transmitted light intensity
+if ~exist('Io', 'var') || isempty(Io)
+    Io = 240;
+end
+
+% OD threshold for transparent pixels
+if ~exist('beta', 'var') || isempty(beta)
+    beta = 0.15;
+end
+
+% tolerance for the pseudo-min and pseudo-max
+if ~exist('alpha', 'var') || isempty(alpha)
+    alpha = 1;
+end
+
+% reference H&E OD matrix
+if ~exist('HERef', 'var') || isempty(HERef)
+    HERef = [
+        0.5626    0.2159
+        0.7201    0.8012
+        0.4062    0.5581
+        ];
+end
+
+% reference maximum stain concentrations for H&E
+if ~exist('maxCRef)', 'var') || isempty(maxCRef)
+    maxCRef = [
+        1.9705
+        1.0308
+        ];
+end
+
+h = size(I,1);
+w = size(I,2);
+
+I = double(I);
+
+I = reshape(I, [], 3);
+
+% calculate optical density
+OD = -log((I+1)/Io);
+
+% remove transparent pixels
+ODhat = OD(~any(OD < beta, 2), :);
+
+% calculate eigenvectors
+[V, ~] = eig(cov(ODhat));
+
+% project on the plane spanned by the eigenvectors corresponding to the two
+% largest eigenvalues
+That = ODhat*V(:,2:3);
+
+% find the min and max vectors and project back to OD space
+phi = atan2(That(:,2), That(:,1));
+
+minPhi = prctile(phi, alpha);
+maxPhi = prctile(phi, 100-alpha);
+
+vMin = V(:,2:3)*[cos(minPhi); sin(minPhi)];
+vMax = V(:,2:3)*[cos(maxPhi); sin(maxPhi)];
+
+% a heuristic to make the vector corresponding to hematoxylin first and the
+% one corresponding to eosin second
+if vMin(1) > vMax(1)
+    HE = [vMin vMax];
+else
+    HE = [vMax vMin];
+end
+
+% rows correspond to channels (RGB), columns to OD values
+Y = reshape(OD, [], 3)';
+
+% determine concentrations of the individual stains
+C = HE \ Y;
+
+% normalize stain concentrations
+maxC = prctile(C, 99, 2);
+
+C = bsxfun(@rdivide, C, maxC);
+C = bsxfun(@times, C, maxCRef);
+
+% recreate the image using reference mixing matrix
+Inorm = Io*exp(-HERef * C);
+Inorm = reshape(Inorm', h, w, 3);
+Inorm = uint8(Inorm);
+
+if nargout > 1
+    H = Io*exp(-HERef(:,1) * C(1,:));
+    H = reshape(H', h, w, 3);
+    H = uint8(H);
+end
+
+if nargout > 2
+    E = Io*exp(-HERef(:,2) * C(2,:));
+    E = reshape(E', h, w, 3);
+    E = uint8(E);
+end
+
+end

test.m

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+I1 = imread('example1.tif');
+I2 = imread('example2.tif');
+
+[Inorm1 H1 E1] = normalizeStaining(I1);
+[Inorm2 H2 E2] = normalizeStaining(I2);
+
+figure('Name', 'Original image 1'), imshow(I1, []);
+figure('Name', 'Normalized image 1'), imshow(Inorm1, []);
+
+figure('Name', 'Original image 2'), imshow(I2, []);
+figure('Name', 'Normalized image 2'), imshow(Inorm2, []);