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classifycells.m
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classifycells.m
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function [cellbw,infocells] = classifycells(varargin)
% CLASSIFYCELLS Classify cells from user defined thresholds
%
% [CELLBW, INFOCELLS] = CLASSIFYCELLS(WAT,IM,PRM) Classify the region in WAT from the image
% IM. PRM is a struct array of settings used in the classification.
% CELLBW is the cell image after classification and INFOCELLS contains
% information about the classification.
%
% PRM.H (default = [0.5 0.5 1.5])
% Voxel size.
%
% 1. PRM.METHOD = 'automated' (default)
% Defines the various modes of classification, with
% the following options:
%
% PRM.MINVOLFULL
% Minimum volume of 3D cell. Default 3. NB This parameter has the given
% name of "FULL" since it should be valid for a full 3D volume. If your
% data is truly 3D nothing is changed about it and PRM.MINVOL = PRM.MINVOLFULL. If your
% data is 2D, this parameter is adjusted automatically towards a 2D
% volume, and a modified parameter PRM.MINVOL is created and used for
% classification.
%
% PRM.MAXVOLFULL
% Maximum volume of 3D cell. Default 100. Also see description for
% PRM.MINVOLFULL above for 2D/3D considerations.
%
% PRM.INTINCELL
% Intensity in cell. Default 0.7.
%
% PRM.INTBORDER
% Intensity of border. Default 1.20.
%
% PRM.CONVEXAREA
% Convex area. Default 0.4.
%
% PRM.CONVEXPERIM
% Convex perimeter. Default 0.35.
%
% PRM.INTBORDER and PRM.INTINCELL are relative thersholds, compared to the
% background. A reduced set of thresholds can be defined by a cell array
% of strings in PRM.PROPNAME. By prm.propname = 'all', all available
% thresholds are used.
%
% 2. PRM.METHOD = 'minimacell'
% The variable MINIMACELL must be defined as
% [CELLBW, INFOCELLS] = CLASSIFYCELLS(...,MINIMACELL)
% Classify the region in WAT from the image im using information in
% MINIMACELL for selecting cells. MINIMIACELL must be a binary image with
% one white region inside each cell, and otherwise black.
%
%
% Ex:
% cprm.minvolfull = 5;
% cprm.maxvolfull = 50;
% [cellbw,infocell] = cellsegm.classifycells(wat,im,cprm);
%
% See also cellsegm.segmct, cellsegm.getminima, cellsegm.segmsurfwat
%
% =======================================================================================
% Copyright (C) 2013 Erlend Hodneland
% Email: erlend.hodneland@biomed.uib.no
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
% =======================================================================================
msg = ['This is ' upper(mfilename) ' for classification of cells and background'];
disp(msg);
wat = varargin{1};
im = varargin{2};
prmin = varargin{3};
% minimacell = ones(size(im));
if nargin == 4
% nucleus minima?
minimacell = varargin{4};
end;
dim = size(im);
if numel(dim) == 2
dim = [dim 1];
end;
%
% Classification parameters
%
% default method
prm.method = 'threshold';
% pixel sizes
prm.h = [0.5 0.5 1.5];
% adjust for 3D
prm.just = 0.9;
% to cut cells
prm.cut = 0;
% minimum and maximum volume
prm.minvolfull = 5;
prm.maxvolfull = 100;
% the intensity threshold relative to the mean of the background
% was at 1.15
prm.intincell = 1.35;
% the intensity threshold on border relative to the mean of the image,
% higher more intensities are required
% was at 1.25
prm.intborder = 1.20;
% the concavity measure, fully concave at 1, set to 0.4-0.5
prm.convexarea = 0.5;
prm.convexperim = 0.35;
% a cell in a marker image
prm.iscellmarker = 1;
% merge input
prm = mergestruct(prm,prmin);
prm.minvolfull = prm.minvolfull*1000;
prm.maxvolfull = prm.maxvolfull*1000;
% voxel volume
prm.voxelvol = prod(prm.h);
% adjust cell volume
[prm.minvol,prm.minvolvox,prm.maxvol,prm.maxvolvox] = cellsegm.cellsize(prm.minvolfull,prm.maxvolfull,prm.h,prm.just,dim(3));
msg = ['Using settings'];
disp(msg);
printstructscreen(prm);
% the integer values to loop over
valwat = unique(wat(wat > 0));
if isequal(prm.method,'threshold')
msg = ['Using thresholds for classification'];
disp(msg);
prm.propname = {'volume',...
'volume',...
'intincell',...
'intborder',...
'convexarea',...
'convexperim'};
prm.thname = {'minvol',...
'maxvol',...
'intincell',...
'intborder',...
'convexarea',...
'convexperim'};
prm.logic = {'gt','lt','gt','gt','gt','gt'};
elseif isequal(prm.method,'minimacell')
msg = ['Using minimacell and volume for classification'];
disp(msg);
prm.propname = {'volume',...
'volume',...
'cellmarker'};
prm.thname = {'minvol',...
'maxvol',...
'iscellmarker'};
prm.logic = {'gt','lt','eq'};
else
error([mfilename ': Wrong option']);
end;
if isequal(prm.method,'threshold') || isequal(prm.method,'minimacell')
msg = ['Thresholds used for classification:'];
disp(msg);
nth = numel(prm.thname);
p = 15;
for i = 1 : nth
name = prm.propname{i};
logic = prm.logic{i};
val = prm.(prm.thname{i});
msg = [makestr(name,p) makestr(logic,p) makestr(num2str(val),p)];
disp(msg);
end;
end;
% number of properties to use
prm.npropname = numel(prm.propname);
%
% Mean int background to adjust the thresholds for intensites
%
% if the image is empty
if isempty(valwat)
cellbw = zeros(size(wat));
infocells = [];
return;
end;
% the number of regions
nwat = length(valwat);
% compute background value
if ~isfield(prm,'meanintbck')
% mean intensity of background (largest region)
[vol,faser] = bwsize(wat > 0,6);
[maxvol,ind] = max(vol);
bck = faser == ind;
prm.meanintbck = mean(im(bck));
end;
% get properties
prop = cellsegm.cellprop(im,wat,valwat,prm.propname,prm.h,prm.meanintbck);
% classify
cellbw = zeros(dim);
for i = 1 : nwat
% this value of WAT
valwathere = valwat(i);
% this region
reghere = eq(wat,valwathere);
if isequal(prm.method,'threshold') || isequal(prm.method,'minimacell')
if isequal(prm.method,'minimacell')
% find overlap to markers
overlap = minimacell .* reghere;
prop.cellmarker(i,1) = ~isempty(find(overlap,1));
end;
nth = numel(prm.thname);
dec = NaN(1,nth);
val = NaN(1,nth);
for j = 1 : nth
str1 = prop.(prm.propname{j})(i);
str2 = prm.(prm.thname{j});
arg = [prm.logic{j} '(' num2str(str1) ',' num2str(str2) ')'];
dec(1,j) = eval(arg);
val(1,j) = prop.(prm.propname{j})(i);
end;
infocells.iscellhere(i,:) = dec;
% is it a cell?
iscell = sum(dec) == nth;
% store
infocells.iscellfinal(i,1) = 0;
if iscell
infocells.iscellfinal(i,1) = 1;
end;
end;
if iscell
cellbw(reghere) = 1;
infocells.iscellfinal(i,1) = 1;
else
infocells.iscellfinal(i,1) = 0;
end;
if iscell == 1
res = 'cell: ';
else
res = 'background: ';
end;
valstr = makestr(res,25);
for j = 1 : numel(val)
valstr = [valstr makestr(num2str(val(j)),15)];
end
valstr = [valstr ' Decisions: ' num2str(dec)];
msg = [' Classifying object ' int2str(i) ' out of ' int2str(nwat) ' as ' valstr];
disp(msg);
end;
% to save
infocells.prm = prm;
infocells.prop = prop;
infocells.propname = prm.propname;
%----------------------------------------------------------