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Image Segmentation of cells

Asked by Mario Trevino on 8 Oct 2012

Hi everybody, Im trying to detect cells in images for which Im using some functions based on watershed segmentation. However, the variable quality in our images makes the segmentation not so reliable, and probably requires some user-input at some point. So, I guess detection has to be semi-automatic, enabling the possibility for user-input to define regions 'by hand'. Has anyone idea if there are allready some tools that solve these problems either jointly or separately?????



Mario Trevino

1 Answer

Answer by Image Analyst on 8 Oct 2012

How would I know if it could be automated unless I see your images? Otherwise, you can use imfreehand() or roipoly() or roipolyold().


Mario Trevino on 8 Oct 2012


Image Analyst on 8 Oct 2012

Seriously? That's your answer? Well maybe I'll download it, get the filename that I downloaded it to, paste it into a load() statement or interactively load it, write the imshow line of code, and look at it eventually if I get bored. In the future, upload only actual images -- not .mat files and not .fig files and not screenshots, unless we ask for those types of files.

In the meantime, here's a demo (that I've posted before) that uses imfreehand to interactively draw into the image. You get a list of coordinates that you can then use for a variety of purposes, like masking as I do in the demo, or to manually draw a line into the image to split objects apart. Perhaps you can find it useful.

% Demo to have the user freehand draw an irregular shape over
% a gray scale image, have it extract only that part to a new image,
% and to calculate the mean intensity value of the image within that shape.
% Also calculates the perimeter, centroid, and center of mass (weighted centroid).
% Change the current folder to the folder of this m-file.
clc;	% Clear command window.
clear;	% Delete all variables.
close all;	% Close all figure windows except those created by imtool.
imtool close all;	% Close all figure windows created by imtool.
workspace;	% Make sure the workspace panel is showing.
fontSize = 16;
% Read in a standard MATLAB gray scale demo image.
folder = fullfile(matlabroot, '\toolbox\images\imdemos');
baseFileName = 'cameraman.tif';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
	% File doesn't exist -- 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 in the search path folders.', fullFileName);
grayImage = imread(fullFileName);
imshow(grayImage, []);
axis on;
title('Original Grayscale Image', 'FontSize', fontSize);
set(gcf, 'Position', get(0,'Screensize')); % Maximize figure.
message = sprintf('Left click and hold to begin drawing.\nSimply lift the mouse button to finish');
hFH = imfreehand();
% Create a binary image ("mask") from the ROI object.
binaryImage = hFH.createMask();
xy = hFH.getPosition;
% Now make it smaller so we can show more images.
subplot(2, 3, 1);
imshow(grayImage, []);
axis on;
title('Original Grayscale Image', 'FontSize', fontSize);
% Display the freehand mask.
subplot(2, 3, 2);
axis on;
title('Binary mask of the region', 'FontSize', fontSize);
% Label the binary image and computer the centroid and center of mass.
labeledImage = bwlabel(binaryImage);
measurements = regionprops(binaryImage, grayImage, ...
    'area', 'Centroid', 'WeightedCentroid', 'Perimeter');
area = measurements.Area
centroid = measurements.Centroid
centerOfMass = measurements.WeightedCentroid
perimeter = measurements.Perimeter
% Calculate the area, in pixels, that they drew.
numberOfPixels1 = sum(binaryImage(:))
% Another way to calculate it that takes fractional pixels into account.
numberOfPixels2 = bwarea(binaryImage)
% Get coordinates of the boundary of the freehand drawn region.
structBoundaries = bwboundaries(binaryImage);
xy=structBoundaries{1}; % Get n by 2 array of x,y coordinates.
x = xy(:, 2); % Columns.
y = xy(:, 1); % Rows.
subplot(2, 3, 1); % Plot over original image.
hold on; % Don't blow away the image.
plot(x, y, 'LineWidth', 2);
drawnow; % Force it to draw immediately.
% Burn line into image by setting it to 255 wherever the mask is true.
burnedImage = grayImage;
burnedImage(binaryImage) = 255;
% Display the image with the mask "burned in."
subplot(2, 3, 3);
axis on;
caption = sprintf('New image with\nmask burned into image');
title(caption, 'FontSize', fontSize);
% Mask the image and display it.
% Will keep only the part of the image that's inside the mask, zero outside mask.
blackMaskedImage = grayImage;
blackMaskedImage(~binaryImage) = 0;
subplot(2, 3, 4);
axis on;
title('Masked Outside Region', 'FontSize', fontSize);
% Calculate the mean
meanGL = mean(blackMaskedImage(binaryImage));
% Put up crosses at the centriod and center of mass
hold on;
plot(centroid(1), centroid(2), 'r+', 'MarkerSize', 30, 'LineWidth', 2);
plot(centerOfMass(1), centerOfMass(2), 'g+', 'MarkerSize', 20, 'LineWidth', 2);
% Now do the same but blacken inside the region.
insideMasked = grayImage;
insideMasked(binaryImage) = 0;
subplot(2, 3, 5);
axis on;
title('Masked Inside Region', 'FontSize', fontSize);
% Now crop the image.
leftColumn = min(x);
rightColumn = max(x);
topLine = min(y);
bottomLine = max(y);
width = rightColumn - leftColumn + 1;
height = bottomLine - topLine + 1;
croppedImage = imcrop(blackMaskedImage, [leftColumn, topLine, width, height]);
% Display cropped image.
subplot(2, 3, 6);
axis on;
title('Cropped Image', 'FontSize', fontSize);
% Put up crosses at the centriod and center of mass
hold on;
plot(centroid(1)-leftColumn, centroid(2)-topLine, 'r+', 'MarkerSize', 30, 'LineWidth', 2);
plot(centerOfMass(1)-leftColumn, centerOfMass(2)-topLine, 'g+', 'MarkerSize', 20, 'LineWidth', 2);
% Report results.
message = sprintf('Mean value within drawn area = %.3f\nNumber of pixels = %d\nArea in pixels = %.2f\nperimeter = %.2f\nCentroid at (x,y) = (%.1f, %.1f)\nCenter of Mass at (x,y) = (%.1f, %.1f)\nRed crosshairs at centroid.\nGreen crosshairs at center of mass.', ...
meanGL, numberOfPixels1, numberOfPixels2, perimeter, ...
centroid(1), centroid(2), centerOfMass(1), centerOfMass(2));
Mario Trevino on 9 Oct 2012

Hey IA! thanks for your demo. Something like it might work. One step before defining regions by hand it would be useful to de-select (with the mouse) blobs that were automatically detected.


binaryImage = imfill(binaryImage, 'holes'); % "hole fill" labeledImage = bwlabel(binaryImage, 8); % Label each blob so we can make measurements of it coloredLabels = label2rgb (labeledImage, 'hsv', 'k', 'shuffle'); % pseudo random color labels

% Get all the blob properties. blobMeasurements = regionprops(labeledImage, imagen_filtrada, 'all'); numberOfBlobs = size(blobMeasurements, 1); % plot axes(handles.axes4); imagesc(coloredLabels);


at this stage, in this plot, it would be cool to press a button in the GUI Im building, and then deselect blobs.... any shortcuts?


ps. hope you are having a better day.

Image Analyst

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