calculate area and parameter cell

i have sputum image. after segmentation, i need to count the area and parameter. this is my coding. after that i dont know how to start calculate area of my image.please help me

%get image from file
image = imread ('C:\Users\acer\Pictures\USM16.jpg');
image1 =image;
figure(1),subplot(3,3,1),imshow(image1),title('original image');

%to grayscale
red=image1(:,:,1);
green=image1(:,:,2);
blue=image1(:,:,3);
image_gray = rgb2gray(image);
figure (1),subplot (3,3,2), imshow(image_gray), title('grayscale');

%tobinary
level = graythresh(image_gray);
BW = im2bw (image_gray,level);
figure(1),subplot(3,3,3),imshow(BW), title('binary');

%closing
se = strel('disk',10);
afterclosing = imclose(BW,se);
figure(1), subplot (3,3,4), imshow(afterclosing), title('closing');

%opening
afteropen = imopen(afterclosing,se);
figure(1), subplot (3,3,5),imshow(afteropen), title('opening');

Answer 1

[John BG]

Hizyan Hanum Hi

Murk Hassan Memon Hi

let me try answer your question.

If you want me to further develop any specific part of my answer just let me know.

If find it useful please mark it as Accepted Answer, thanks in advance:

1.

Capture
A=imread('sample1.jpg');
title('initial image');

2.

sometimes the variance between RGB layers shows a clear boundary

figure(5);varA=surf(var(double(A),0,3));

.

B=varA.CData;
B(B<1000)=0;
B(B>=1000)=255;
figure(6);imshow(B)

.

3.

RGB split

Because with the variance the cells look 'dehydrated', the RGB split shows

ColorList={'Red' 'Green' 'Blue'};
N=255;gr=0:1/(N-1):1;  
figure(1);imshow(A);

cMap=zeros(N,3);cMap(:,1)=gr;
figure(2);hr=imshow(ind2rgb(A(:,:,1),cMap));title(ColorList{1});
% filtering greens
cMap=zeros(N,3);cMap(:,2)=gr;
figure(3);hg=imshow(ind2rgb(A(:,:,2),cMap));title(ColorList{2});
% filtering blues
cMap=zeros(N,3);cMap(:,3)=gr;
figure(4);hb=imshow(ind2rgb(A(:,:,3),cMap));title(ColorList{3});

C=hg.CData;
figure(7);imshow(C);

C=C(:,:,2);C=255*C;
C(C>150)=255;
C(C<150)=0;
figure(8);imshow(C);

.

4.

total area, in pixels

numel(C)
 =
       198660

area in pixels

   numel(nonzeros(~C))
      =
             6172

same as

     [cx,cy]=find(~C);
    numel(cx)
      =
             6172

in percentage

    100*numel(nonzeros(~C))/numel(C)
       =
       3.106815664955200

there is a 3.1% are of red tagged cells

5.

and following the MATLAB example using RGB to L*a*b and K-means clustering

A=imread('sample1.jpg');
figure(1);imshow(A)
cform = makecform('srgb2lab');                                                      % RGB to L*a*b color space
lab_he = applycform(A,cform);

ab = double(lab_he(:,:,2:3));
nrows = size(ab,1);
ncols = size(ab,2);
ab = reshape(ab,nrows*ncols,2);

nColors = 3;                                                                                        % sort out  with K-means clustering
% repeat the clustering 3 times to avoid local minima
[cluster_idx, cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', 'Replicates',3);

pixel_labels = reshape(cluster_idx,nrows,ncols);                        % label all pixels !?
figure(2);imshow(pixel_labels,[]), title('image labeled by cluster index');

segmented_images = cell(1,3);
rgb_label = repmat(pixel_labels,[1 1 3]);

for k = 1:nColors                                                                                       %  sub-images segmenting A by color
    color = A;
    color(rgb_label ~= k) = 0;
    segmented_images{k} = color;
end

figure(3);imshow(segmented_images{1}), title('objects in cluster 1');
figure(4);imshow(segmented_images{2}), title('objects in cluster 2');

. If the tagging has caught all the cells of interest and only the cells of interest (assuming the small specks do not contain red tagging) then a more accurate area percentage would be

C=segmented_images{2};
C1=C(:,:,1);imshow(C1)
C1(C1>100)=255;C1(C1<100)=0;
100*numel(nonzeros(C1))/numel(C1)
 =
   2.281787979462398

thanks for time and attention, any feedback welcome awaiting answer

John BG

<mailto:[e-mail] [e-mail]>

[murk hassan memon]

well done .. thank u very much

Answer 2

[Image Analyst]

See my Image Segmentation Tutorial http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A31862 and you'll know how to do things like:

labeledImage = bwlabel(afteropen);
measurements = regionprops(labeledImage, 'Area', 'Perimeter');
allAreas = [measurements.Area];
allPerimeters = [measurements.Perimeters];

[Image Analyst]

By the way, don't call your variable image since that is the name of a built in function. By the way, there are other problems with your code. You don't do anything with image1, red, blue, or green. You convert the image to gray scale. You use graythresh() and im2bw - not good unless you know for certain that there are a significant number of red cells in the picture. It's better to use a fixed threshold. That will work for any amount of red cells from 0% to 100% area fraction of red cells.

Looks like you're trying to smooth the boundaries with a single pass of an alternating sequential filter You can also use activecontour() for that - demo attached.

[izyan hanum]

 i understand, now i change the variable image to sputum. its will more okay right.? so i need to do red blue green before grayscale or after grascale? im not understand with the color. can you help me.?

[Image Analyst]

I gave you a full script in your duplicate question.

[izyan hanum]

thank you so much :) i will try my best

Answer 3

[murk hassan memon]

I want code for the infected cells from blood sample images if any one have any idea regarding this topic then kindly help me out

Answer 4

[Natalia Matviychuk]

Anyone knows how to calculate the cytoplasm area (all area- nucleus area)??

Help me please

[Image Analyst]

Did you try the image segmentation tutorial in my answer? If not, why not? It shows you how to use regionprops to measure areas.

[Natalia Matviychuk]

Yes, the tutorial is very nice, I understand how to measure all area of the coin but I wanted to measure a cytoplasm area(not all area of the cell). For example i have this image, and I need to measure the grey area

[Image Analyst]

I think you didn't try to adjust the thresholds, did you? See this code, which produces:

allreas =

3440 3987

% Initialization steps.
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
imtool close all;  % Close all imtool figures if you have the Image Processing Toolbox.
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 22;
% Check that user has the Image Processing Toolbox installed.
hasIPT = license('test', 'image_toolbox');
if ~hasIPT
	% User does not have the toolbox installed.
	message = sprintf('Sorry, but you do not seem to have the Image Processing Toolbox.\nDo you want to try to continue anyway?');
	reply = questdlg(message, 'Toolbox missing', 'Yes', 'No', 'Yes');
	if strcmpi(reply, 'No')
		% User said No, so exit.
		return;
	end
end

%===============================================================================
% Read in a demo image.
folder = pwd
baseFileName = '37NT.bmp';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
if ~exist(fullFileName, 'file')
	% Didn't find it there.  Check the search path for it.
	fullFileName = baseFileName; % No path this time.
	if ~exist(fullFileName, 'file')
		% Still didn't find it.  Alert user.
		errorMessage = sprintf('Error: %s does not exist.', fullFileName);
		uiwait(warndlg(errorMessage));
		return;
	end
end
grayImage = imread(fullFileName);
% Get the dimensions of the image.  numberOfColorBands should be = 3.
[rows, columns, numberOfColorBands] = size(grayImage);
if numberOfColorBands > 1
	fprintf('This image is RGB.  I will change it to gray scale.\n');
	grayImage = grayImage(:, :, 2);
end

% Display the original image.
subplot(1, 2, 1);
imshow(grayImage);
axis('on', 'image');
caption = sprintf('Original Image : "%s"', baseFileName);
title(caption, 'FontSize', fontSize);
impixelinfo;
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'Outerposition', [0, 0.1, 1, 0.9], ...
	'Name', 'Demo by Image Analyst', 'NumberTitle', 'Off');

% Get gray stuff only.
binaryImage = grayImage > 112 & grayImage < 240;
% Display the original image.
subplot(1, 2, 2);
imshow(binaryImage);
axis('on', 'image');
caption = sprintf('Binary Image');
title(caption, 'FontSize', fontSize);
impixelinfo;

% Make area measurements.
props = regionprops(binaryImage, 'Area');
allreas = [props.Area]

Answer 5

[Natalia Matviychuk]

Thank you sooo much! And could you explain me please why did you used values 112 and 240 ?

binaryImage = grayImage > 112 & grayImage < 240;

[Image Analyst]

I just took values that were half way between the gray and black, and the gray and white. I could have just said

binaryImage = grayImage == 235;

but that image didn't look like a read limage - it looked like computer graphics. Perhaps a synthetic image you made up for testing, and if so, a real image would not be just at one single gray level but would have a range of gray levels. So I took a range of gray levels to try to let it work should you ever get a real image. Of course you can adjust those values however you need to.