function mmqt_segment_image(trunk, figureScaling, figureHide)

% function mmqt_segment_image(fnameCZI, figureScaling, figureHide)

% function mmqt_segment_image(fnameMat, figureScaling, figureHide)

%

% Segment foreground from background. In particular, cell nuclei and

% micgroglia will be segmented from the coresponding color layer.

% Input should be a Z-stack with two color layers:

% 1. Layer: DAPI staining of nuclei

% 2. Layer: anti-Iba1 steining of microglia

%

% Input arguments:

% fnameCZI - path to CZI-file; note however, that this path-name

% will only be used to reconstruct the path-name of the MAT-file with the raw data (see below).

% In order to be found, the correspinding MAT-file should be stored in the same folder as the CZI-file.

% fnameMat - path to MAT-file, which contains a 4D matrix with the raw image matrix and a

% structure with the information about image size and scaling.

% This MAT-file can be created by reading CZI-files using the script: mmqt_read_convert_czi_files.m

% figureScaling - number, indicating how big the figures should be displayed on the screen;

% relevant only for the figures showing orthogonal slices through the stack;

% a value of one means that one pixel on the screen correponds to one pixel in the image

% (optional, defaults to figureScaling=2.5)

% figureHide - logical, indicating whether to make figures visible or not

% If figureHide=true, figures will be made invisible and used only for saving picturs of the screenshot;

% (optional, defaults to figureHide=fasle)

%%

fprintf('\n\nSegmenting image:\n\n')

%% decompose pathname

[folder, basename, ~] = fileparts(trunk);

basename = regexprep(basename, '_stack1_raw$', ''); %--- in case the MAT-file is given, remove the suffix, which is added by mmqt_read_convert_czi_files.m

trunk = fullfile(folder, basename);

%% declare default values for unspecified variables

varNames = {'figureScaling', 'figureHide'};

varDefault = {'2.5', 'false'};

for i=1:length(varNames)

if ~exist(varNames{i}, 'var') || isempty(eval(varNames{i}))

eval([varNames{i} '= ' varDefault{i} ';'])

end

end

%% Specify some parameters

rSoma = 2.2;

thrVolume = 10;

%% Create log file

hTimer = tic;

fnameLog = [trunk, '_log1_segmentation.txt'];

fid = fopen(fnameLog, 'w');

fprintf(fid, '%s\n', date);

fclose(fid);

%% Reconstruct MAT file name with raw data

fname = [trunk '_stack1_raw.mat'];

if ~exist(fname, 'file')

fname = [trunk '.mat'];

if ~exist(fname, 'file')

error('%s\n%s\n %s\n %s\n', 'File with raw data Z-stack not found!', 'The file-name was expected to be either one of these:', [trunk '_stack1_raw.mat'], [trunk '.mat'])

end

end

%% Read MAT file

[hdr, img1] = read_3D_image_matrix(fname);

load(fname, 'img', 'hdr');

%% normalize to range [0,1]

img2 = img1./max(img1(:));

%% smooth the data

tee(fnameLog, 'Smoothing the data slice by slice... \n')

imgS = zeros(size(img2));

sigma = 0.3 ./ hdr.pixdim(2:3); %--- pixel dimension is given in micrometer

%--- display in command window

tee(fnameLog, '- Voxel dimension: %.3f %.3f %.3f\n', hdr.pixdim(2:4))

tee(fnameLog, '- Sigma: %g %g\n', sigma)

%--- smooth the data

tic

for iL=1:size(img2,4)

%--- slice by slice

for iS=1:size(img2,3)

imgS(:,:,iS,iL) = imgaussfilt(img2(:,:,iS,iL),sigma);

end

end

tee(fnameLog, display_time_delay)

%% check out histograms and effective resolution (number of discrete levels used to encode the 1st-99th percentile range)

[hf, ~, prctD, histD] = volume_histogram_by_slice_and_color(img1, 45, [], [], [], figureHide);

%% save picture of histogram

%--- histogram

F = getframe(hf(1));

imwrite(F.cdata, [trunk,'_prepro2_histograms_raw.png'])

%--- effective resolution

F = getframe(hf(2));

imwrite(F.cdata, [trunk,'_prepro1_effective_resolution.png'])

%% spatial correlations

thrSpatialCorr = 0.78;

tee(fnameLog, 'Calculate spatial correlations ... ')

tic

%--- Z direction (correlations between succesive slices)

%- Note: spatialCorrSmoothed is not used, but saved in a mat-file

[h, idxSliceOk, spatialCorrSmoothed] = spatial_correlations_in_stack(img2, 'vertical', thrSpatialCorr, figureHide);

%--- save picture of spatial correlations (across slice)

F = getframe(h);

imwrite(F.cdata, [trunk,'_prepro1_spatial_corrZ.png'])

%--- XY direction (within slice)

[h] = spatial_correlations_in_stack(img2, 'horizontal', thrSpatialCorr, figureHide);

%--- save picture of spatial correlations (within slice)

F = getframe(h);

imwrite(F.cdata, [trunk,'_prepro1_spatial_corrXY.png'])

tee(fnameLog, display_time_delay)

%%

if any(isnan(idxSliceOk))

warning('None of the slices has the required quality, according to spatial correlations!')

warning('Analysing the whole stack!')

idxSliceOk = [1 size(img1,3)];

%--- write warning to log file

fid = fopen([trunk,'_warnings.txt'], 'a');

fprintf(fid, 'None of the slices has the required quality, according to spatial correlations!\n');

fprintf(fid, ' => Analysing the whole stack!\n');

end

%% save result of spatial correlations in z-direction

save([trunk,'_prepro1_spatial_corrZ_sliceOk.mat'], 'idxSliceOk', 'spatialCorrSmoothed', 'thrSpatialCorr')

%% equalize histograms

%--- define a norm histogram to which all histograms shell be matched using histeq.m

for iL=1:size(prctD,3)

%--- as norm, select percentiles from slice with largest difference between the 99th and 1th percentile

prctRange = prctD(2,:,iL)-prctD(1,:,iL);

prctRmax = max(prctRange);

%--- consider also slices where the criterion has a value of at least 95% of the maximum

idx = find(prctRange>prctRmax*0.95);

%--- calculate the norm histogram as the mean histogram across the selected slices

histNorm(:,iL) = mean(histD(:,idx,iL),2);

end

%--- equalize histograms using "histeq"

imgEq = zeros(size(img2));

for iL = 1:size(img2,4)

for iS = 1:size(img2,3)

imgEq(:,:,iS,iL) = histeq(img2(:,:,iS,iL),histNorm(:,iL));

end

end

%% plot histogram

hf = volume_histogram_by_slice_and_color(imgEq, [], [], [], [], figureHide);

%%

F = getframe(hf);

imwrite(F.cdata, [trunk,'_prepro3_histograms_equalized.png'])

%% normalize each color layer to a certain percentile range (for visualization)

prct = prctile(reshape(imgEq(:,:,:,1),[],1),[1 99]);

imgEq(:,:,:,1) = imgEq(:,:,:,1) - prct(1);

imgEq(:,:,:,1) = imgEq(:,:,:,1)./diff(prct);

prct = prctile(reshape(imgEq(:,:,:,2),[],1),[1 99]);

imgEq(:,:,:,2) = imgEq(:,:,:,2) - prct(1);

imgEq(:,:,:,2) = imgEq(:,:,:,2)./diff(prct);

imgEq(imgEq<0) = 0;

imgEq(imgEq>1) = 1;

%% show the whole stack

h = show_cells_3D(imgEq,hdr,[],[],figureScaling, [], figureHide);

%--- save picture

F = getframe(h);

imwrite(F.cdata, [trunk,'_ortho2_equalized.png']);

%% crop the stack

imgEqCrop = imgEq(:,:,idxSliceOk(1):idxSliceOk(2),:);

imgSCrop = imgS(:,:,idxSliceOk(1):idxSliceOk(2),:);

% imgSCrop = imgS;

%% show the cropped stack

h = show_cells_3D(imgEqCrop,hdr,[],[],figureScaling, [], figureHide);

%--- save picture

F = getframe(h);

imwrite(F.cdata, [trunk,'_ortho3_equalizedCrop.png']);

%% Determine threshold for each slice separately

tee(fnameLog, 'Determine thresholds for each slice... \n')

tic

hf = figure;

if figureHide

hf.Visible = 'off';

end

ha = axes();

% signalVar = zeros(size(img1Crop,3),2);

% noiseVar = zeros(size(img1Crop,3),2);

thrE_LTS = zeros(2,4, size(imgSCrop, 3));

fprintf('- Working on slice ')

strLoop = [];

for iS = 1:size(imgSCrop,3)

%% feedback about analysed slice number on screen

fprintf(repmat('\b',1,length(strLoop)));

strLoop = sprintf('%d out of %d', iS, size(imgSCrop,3));

fprintf('%s', strLoop);

%% work with a single slice and all color channels

C = squeeze(imgSCrop(:,:,iS,:));

% C(:,:,3) = 0;

%% calculate edges

CE = C;

method = 'Sobel';

CE(:,:,1) = edge(C(:,:,1),method);

CE(:,:,2) = edge(C(:,:,2),method);

%% define thresholds using histogram for edge intensities

%--- Extract intensities on edges froeach channel

Red = C(:,:,1);

Green = C(:,:,2);

Red = Red(CE(:,:,1)==1);

Green = Green(CE(:,:,2)==1);

% Blue = C(:,:,3);

%--- Get histValues for each channel

[yRed, xR] = imhist(Red,128);

[yGreen, xG] = imhist(Green,128);

if ~all(xR==xG)

error('Histogram binning for red and green is not matched!')

end

x = xR;

% [yBlue, xB] = imhist(Blue);

%--- Plot them together in one plot

hold off

plot(x, yRed, 'Red', x, yGreen, 'Green') %, xB, yBlue, 'Blue');

hold on

xlabel('intensity (normalized)')

ylabel('count')

ha.FontSize = 14;

%--- fit Kernel distribution

pdR = fitdist(Red,'Kernel','Support','positive');

plot(x,pdR.pdf(x) * sum(yRed)/127 ,'k','LineWidth',1)

pdG = fitdist(Green,'Kernel','Support','positive');

plot(x,pdG.pdf(x) * sum(yGreen)/127 ,'k','LineWidth',1)

thrE = zeros(2,4);

%--- use percentiles as threshold levels

x = 0:0.001:1;

cdfT = pdR.cdf(x);

%- 5th percentile

[~, idx] = min((cdfT-5/100).^2);

locL1 = x(idx);

%- 20th percentile

[~, idx] = min((cdfT-20/100).^2);

locM = x(idx);

%-----

thrE(1,:) = [locL1 locM NaN NaN];

%--- use percentiles as threshold levels

cdfT = pdG.cdf(x);

%- 25th percentile

[~, idx] = min((cdfT-25/100).^2);

locM = x(idx);

%- 55th percentile

[~, idx] = min((cdfT-55/100).^2);

locU1 = x(idx);

%-----

thrE(2,:) = [NaN locM locU1 NaN];

%--- plot threshold levels

color='rg';

for i=1:size(thrE,1)

for j=1:size(thrE,2) -1

plot([thrE(i,j) thrE(i,j)],ylim,color(i),'LineWidth',2)

% text(thrE(i,j),diff(ylim)*(i/(3+j*0.1)),num2str(thrE(i,j)),'FontSize',14)

end

end

%--- add text at the end to avoid obstruction by lines

for i=1:size(thrE,1)

for j=1:size(thrE,2) -1

text(thrE(i,j),diff(ylim)*(i/(3+j*0.2)),num2str(thrE(i,j)),'FontSize',14)

end

end

%---

drawnow

%% collect thresholds for all slices

thrE_LTS(:,:,iS) = thrE;

end

fprintf(' ')

tee(fnameLog, display_time_delay)

close(hf)

%% smooth the thresholds

thrE_TLS_s = nan(size(thrE_LTS));

%x = (1:size(thrE_LTS,3))';

for iL = 1:size(thrE_LTS,1)

for iT = 1:size(thrE_LTS,2)

if ~all(isnan(thrE_LTS(iL,iT,:)))

%--- smooth the curve

y =squeeze(thrE_LTS(iL,iT,:));

%--- smooth with moving average and find outliers

thrE_TLS_s(iL,iT,:) = smooth_moving_average(y, 1);

end

end

end

%% show the smoothed thresholds

h = figure;

if figureHide

h.Visible = 'off';

end

h.Position = [1032 537 752 808];

x = idxSliceOk(1):idxSliceOk(2);

subplot(2,1,1); hold on

plot(x, squeeze(thrE_LTS(1,:,:))','LineWidth',1)

plot(x, squeeze(thrE_TLS_s(1,:,:))','k')

set(gca,'FontSize',14)

title('Thresholds for red layer')

ylabel('intensity (normalized)')

subplot(2,1,2); hold on

plot(x, squeeze(thrE_LTS(2,:,:))','LineWidth',1)

plot(x, squeeze(thrE_TLS_s(2,:,:))','k')

set(gca,'FontSize',14)

title('Thresholds for green layer')

xlabel('slice')

ylabel('intensity (normalized)')

% legend({'lower 2/3 max','maximum','upper 2/3 max','upper 1/3 max'})

legend({'thr1','thr2','thr3','thr4'})

%% save picture

F = getframe(h);

imwrite(F.cdata, [trunk,'_prepro4_thresholds.png'])

%% Threshold the image

tee(fnameLog, 'Thresholding image slice by slice... \n')

tic

%--- Declare variable for thresholded 3D output volume

imgThr = zeros(size(imgSCrop),'uint8');

%---

fprintf('- Working on slice ')

strLoop = [];

for iS = 1:size(imgSCrop,3)

% clearvars -except iSlice img1Crop imgT img hdr imgSegmentation

% close all

%% feedback about analysed slice number on screen

fprintf(repmat('\b',1,length(strLoop)));

strLoop = sprintf('%d out of %d', iS, size(imgSCrop,3));

fprintf('%s', strLoop);

%% work with a single slice and all color channels

C = squeeze(imgSCrop(:,:,iS,:));

thrE = thrE_TLS_s(:,:,iS);

%% segment microglia cells

%--- threshold the image

BW1 = C(:,:,2) > thrE(2,3); %--- get green with a high threshold

BW2 = C(:,:,2) > thrE(2,2) & C(:,:,1) < thrE(1,1); %--- green with low threshold (maximum of int at edges) but excluding red

BW = BW1 | BW2;

%% segment nuclei

BWnuclei = C(:,:,1) > thrE(1,2) & C(:,:,2) > thrE(2,3); %--- get the nuclei

%% insert thresholded slice into 3D volume

imgThr(:,:,iS, 1) = BWnuclei;

imgThr(:,:,iS, 2) = BW;

end

fprintf(' ')

tee(fnameLog, display_time_delay)

%% remove small cluster from green layer

tee(fnameLog, 'Removing small cluster from green layer ... ')

tic

%--- clusterize

L = bwlabeln(imgThr(:,:,:,2));

%--- get area of cluster

T = regionprops('table', L, 'centroid', 'area');

%--- x/y dimension are exchanged by "regionprops"

S = [];

S.cogI = T.Centroid(:,[2 1 3]);

S.cog = S.cogI .* repmat(hdr.pixdim(2:4), height(T), 1) - repmat(hdr.pixdim(2:4)./2, height(T), 1);

S.voxN = T.Area;

S.voxV = T.Area * prod(hdr.pixdim(2:4));

%--- find small cluster with threshold

S.smallCluster = S.voxV < thrVolume;

%--- remove small cluster

idxBig = find(S.smallCluster==0);

IG = ismember(L,idxBig);

tee(fnameLog, display_time_delay)

%% %% Fill holes

tee(fnameLog, 'Fill holes ingreen layer ... ')

tic

IG = imfill(IG,'holes');

% IGF = imfill(IG,'holes');

tee(fnameLog, display_time_delay)

%% Define the soma of the cells, by removing the branches from it

%--- create structuring element

%- radius of sphere

rSomaPreliminary = 1.5;

dim = hdr.pixdim(2:4);

sesz = rSomaPreliminary ./ dim;

%--- center of the sphere

secr = round(sesz) + 1;

%--- size of bounding box

sesz = 2 * round(sesz) + 1;

%--- distance from center

ne = zeros(sesz);

for i=1:size(ne,1)

for j=1:size(ne,2)

for k=1:size(ne,3)

ne(i,j,k) = sum(([i j k] .* dim - secr .* dim).^2).^.5;

end

end

end

%--- voxel within requested radius from center

ne(ne>rSomaPreliminary) = 0;

ne(secr(1),secr(2),secr(3)) = 1;

ne = logical(ne);

%--- visualize the spherical structuring element

% show_cells_3D(ne,hdr,[],[],10);

%---

seSoma = strel(ne);

%--- open the green layer to remove arms

tee(fnameLog, 'Define soma ... ')

tic

IS = imopen(IG,seSoma);

tee(fnameLog, display_time_delay)

%% Fill small gaps in MaskCell (green layer) in the immediate vicinity of the soma

%- Two-step procedure:

%- 1. Dilate the soma (smoothed by imopen above) to restrict the gap filling operation to the immediate surrounding of the soma

%- 2. Fill gaps in MaskCell, using the Matlab function imclose with a spherical structuring element of radiaus 1.2 microns

%--- Create structuring element

rDilate = 1.2;

dim = hdr.pixdim(2:4);

sesz = rDilate ./ dim;

%--- center of the sphere

secr = round(sesz) + 1;

%--- size of bounding box

sesz = 2 * round(sesz) + 1;

%

ne = zeros(sesz);

for i=1:size(ne,1)

for j=1:size(ne,2)

for k=1:size(ne,3)

ne(i,j,k) = sum(([i j k] .* dim - secr .* dim).^2).^.5;

end

end

end

ne(ne>rDilate) = 0;

ne(secr(1),secr(2),secr(3)) = 1;

ne = logical(ne);

%---

% show_cells_3D(ne,hdr,[],[],10);

%---

seDilate = strel(ne);

%--- dilate the soma

tee(fnameLog, 'Dilate soma mask ... ')

tic

ID = imdilate(IS,seDilate);

tee(fnameLog, display_time_delay)

%--- imclose the green layer

tee(fnameLog, 'Close small gaps in green layer ... ')

tic

IGc = imclose(IG,seDilate);

tee(fnameLog, display_time_delay)

%--- mask closed green layer with dilated soma to restrict the closing operation to the surrounding of the soma

ID = IGc & ID;

%--- add the closed soma to the green layer

IG = IG | ID;

%% Mask the nuclei exclusively with the MaskSoma to exclude voxel cluster which might be artefacts.

%--- For example, if a branch lies in close proximity to a non-microglia nucleus,

%--- an overlap of red and green might result, erroneously indicating the presence

%--- of a microglia nucleus in MaskNuclei.

IN = imgThr(:,:,:,1) & IS;

%% remove small cluster of potential nuclei to exclude those, which are actually not a soma but have arteficial red pixel in it

%--- clusterize

L = bwlabeln(IN);

%--- get area of cluster

T = regionprops('table', L, 'centroid', 'area');

%--- x/y dimension are exchanged by "regionprops"

S = [];

S.cogI = T.Centroid(:,[2 1 3]);

S.cog = S.cogI .* repmat(hdr.pixdim(2:4), height(T), 1) - repmat(hdr.pixdim(2:4)./2, height(T), 1);

S.voxN = T.Area;

S.voxV = T.Area * prod(hdr.pixdim(2:4));

%--- find small cluster with threshold

S.smallCluster = S.voxV < thrVolume;

%--- remove small cluster

idxBig = find(S.smallCluster==0);

IN = ismember(L,idxBig);

%% Now redefine the soma in the repaired mask, by removing the branches from it, but using a larger diameter

%--- create structuring element

%- radius of sphere

dim = hdr.pixdim(2:4);

sesz = rSoma ./ dim;

%--- center of the sphere

secr = round(sesz) + 1;

%--- size of bounding box

sesz = 2 * round(sesz) + 1;

%--- distance from center

ne = zeros(sesz);

for i=1:size(ne,1)

for j=1:size(ne,2)

for k=1:size(ne,3)

ne(i,j,k) = sum(([i j k] .* dim - secr .* dim).^2).^.5;

end

end

end

%--- voxel within requested radius from center

ne(ne>rSoma) = 0;

ne(secr(1),secr(2),secr(3)) = 1;

ne = logical(ne);

%--- visualize the spherical structuring element

% show_cells_3D(ne,hdr,[],[],10);

%---

seSoma = strel(ne);

%--- open the green layer to remove arms

tee(fnameLog, 'Redefine soma after gap closure ... ')

tic

IS = imopen(IG,seSoma);

tee(fnameLog, display_time_delay)

%% Add nuclei to the soma mask

%- to account for the possibility that a nucleus is not anymore inside a soma, after redifining them with a different diameter

IS = IS | IN;

%% accept soma only, if nucleus can be found within

L = bwlabeln(IS);

idxSoma = unique(L(IS & IN));

IS = ismember(L,idxSoma);

%% Dilate the redefined soma, for following purposes:

%- 2. separate real branches from minor bumps on its surface

%- 3. separate branches which share the same basis or are connected by ridges on the surface of the soma

%---

tee(fnameLog, 'Dilate soma ... ')

tic

ID = imdilate(IS,seDilate);

ID = IG & ID;

tee(fnameLog, display_time_delay)

%% accept dilated-soma only, if nucleus (soma) can be found within (this step is needed, because during the dilation step, the dilation can swap across background and create disconnected areas)

L = bwlabeln(ID);

idxSomaDil = unique(L(ID & IN));

ID = ismember(L,idxSomaDil);

%% final result of segmentation: arms + border + soma + nuclei

%--- summing up results in following labels:

%- arms = 1

%- border = 2

%- soma = 3

%- nuclei = 4

IABSN = uint8(IG + ID + IS + IN);

cmap = [0 0 0; 0 0.8 0; 0.8 0.3 1; 0 0 1; 1 1 0];

%--- show final result

h = show_cells_3D(IABSN,hdr,[],[],figureScaling, cmap, figureHide);

%--- save picture

F = getframe(h);

imwrite(F.cdata, [trunk,'_ortho4_segmented.png']);

%% histogram of intensities within the nuclei and microglia segments

%--- create mask

if idxSliceOk(1) > 1

temp1 = zeros(size(IN,1), size(IN,2), idxSliceOk(1)-1, 2);

else

temp1 = [];

end

if idxSliceOk(2) < size(img1,3)

temp2 = zeros(size(IN,1), size(IN,2), size(img1,3)-idxSliceOk(2), 2);

else

temp2 = [];

end

mask = cat(3, temp1, cat(4, IN, IG), temp2);

%---

hf = volume_histogram_by_slice_and_color(img1, 20, mask, [], [], figureHide);

%% save picture

%--- histogram

F = getframe(hf(1));

imwrite(F.cdata, [trunk,'_prepro5_histograms_segmented.png'])

%%

tee(fnameLog, 'Save images ... ')

tic

%% save data: equalized image

fnameOut = [trunk, '_stack2_equalized.mat'];

%--- convert image to 8 bit color depth

write_3d_image_matrix(hdr, uint8(imgEq*255), fnameOut)

%% save data: final segmentation

fnameOut = [trunk, '_stack3_segmented.mat'];

hdr2 = change_hdr_datatype_info(hdr, 2);

hdr2 = change_hdr_dim(hdr2, size(IABSN));

hdr2.cmap = cmap;

write_3d_image_matrix(hdr2, uint8(IABSN), fnameOut)

%%

tee(fnameLog, display_time_delay)

%% Make a video

hf = figure;

if figureHide

hf.Visible = 'off';

end

r = groot;

tee(fnameLog, 'Make movie ...')

tic

movName = [trunk,'_video_soma_cell'];

v = VideoWriter(movName, 'MPEG-4');

v.FrameRate = 9;

open(v)

for iS=1:size(IABSN,3)

temp = rot90(squeeze(IABSN(:,:,iS)));

temp = ind2rgb(temp,cmap);

C = rot90(squeeze(imgEqCrop(:,:,iS,:)));

%--- normalize to a certain percentile range

prct = prctile(reshape(C(:,:,1),[],1),[1 99]);

C(:,:,1) = C(:,:,1) - prct(1);

C(:,:,1) = C(:,:,1)./diff(prct);

prct = prctile(reshape(C(:,:,2),[],1),[1 99]);

C(:,:,2) = C(:,:,2) - prct(1);

C(:,:,2) = C(:,:,2)./diff(prct);

C(C<0) = 0;

C(C>1) = 1;

%---

C(:,:,3) = 0;

%--- flip colors

C = C(:,:,[3 2 1]);

%---

temp3 = ones(size(IABSN,1), 2, 3);

r.CurrentFigure = hf;

imshow(cat(2, C, temp3, temp), [])

writeVideo(v,getframe(hf))

end

close(v)

close(hf)

tee(fnameLog, display_time_delay)

%%

tee(fnameLog, 'Segmentation completed!\n')

tee(fnameLog, display_time_delay(hTimer))

fprintf('\n\n')