Draw partial white border line in an object in grayscale

Hi all. This could be easy to some (or probably most) but sadly not me.

Here's original image: http://www.imagesup.net/?di=1513591333332

White blob of above image (if that matter): http://www.imagesup.net/?di=1213591337642

And here's what the output image should be: http://www.imagesup.net/?di=1113591338872

Is that possible to add/draw that white line as per output image?

Your helps are much appreciated.

DE

Answer 1

[Image Analyst]

How did you draw the red line in the first place? Since you have that (or SHOULD have that), it will be easy to just scan the image writing a white dot at each location.

[Demons]

Red line was drawn with intention to remove the lower part of the image using virtually any photo editing apps available (such as Paint, etc). Without that, it's nearly difficult (well at least to me) to mark the outer bone region (black round pixel area) once the unwanted muscles region removed. Have you any idea the most efficient way to further process instead of putting red line?

[Image Analyst]

The answer is to not bother wasting time drawing that red line in the first place. It's a total waste of time. Look over this demo and I'll show you how you can do it with imfreehand and totally skip the photo editor step.

% 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.
if(~isdeployed)
  cd(fileparts(which(mfilename)));
end
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);
    uiwait(warndlg(errorMessage));
    return;
  end
end
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');
uiwait(msgbox(message));
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;
drawnow;
title('Original Grayscale Image', 'FontSize', fontSize);

% Display the freehand mask.
subplot(2, 3, 2);
imshow(binaryImage);
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);
imshow(burnedImage);
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);
imshow(blackMaskedImage);
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);
imshow(insideMasked);
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);
imshow(croppedImage);
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));
msgbox(message);

[Demons]

Hi IA. Really appreciate your effort as always. But the results seem far from what I wanted it to be.

How if I replace the red marker to black and once converted to grayscale as a result there would be 2 sections (above and bottom area). Any way to remove unwanted area by its location (in this case bottom ones)?

[Image Analyst]

Please explain to me why this is a two step process, with the first part being unnecessary. Is the initial manual segmentation being done in red being outsourced to some country with cheap labor or something? And then you have to come along and finish the job automatically? Why can't the people who do the red outining just use imfreehand??? If I understand this then I'll tell you how.

[Demons]

No outsource labour used or any sort. Just me. If you scroll back to previous texts, the primary objectives are to reduce as much time and effort as possible. If I run method you suggested above, I have to consistently focus on one image, in which as a result took longer time to process.

Say I've got 100 of such images, if I manually draw the red (or any colour if that matter) marker to divide the wanted and unwanted area, then stash them to MATLAB to do the magic, I'd think the processing time will be greatly reduced.

The idea is to segment the ROI and get the area (pixel value) of gray area (muscles), black (outer bone) and white (inner bone).

I must admit that my OP wasn't that clear and the requirement of white border line is totally from my beginner MATLAB brain. I just thought with that white border line I could then easily compute the ROIs above, especially the outer bone part (black pixel).

[Image Analyst]

Well I think you're wrong - you're doubling the amount of code. But this is how you do that

First draw the dividing line using imfreehand() or however you're doing it. Convert the image to true color. Take the coordinates and write the red path as a colored curve into the image. Now, instead of masking out that portion and being done with everything, save the image. You're doing all of this already. Now for the next step (which normally wouldn't be necessary).

Read in the color image. Take the red channel and the blue channel and find the red line by doing

redPixels = redChannel == 255 & blueChannel == 0;

Now scan the image column by column using find to find the lowest row that is red

[rows columns] = size(redPixels);
lowestRow = columns * ones(1, columns); % Array to hold lowest row.
for col = 1 : columns
  lowRow = find(redPixels(:, col), 1, 'last')
  if ~isempty(lowRow)
    % There is some red in that column.  Save it.
    lowestRow(col) = lowRow;
  end
end

Now you have the location of the bottom of that wide red swath you drew. Essentially it will be the same as the path you drew with imfreehand() but a little lower since you drew in a thick red path. Now you can use poly2mask() to create a mask from those coordinates and mask it off using code I already gave you above.

[Demons]

Thanks IA. Your input, time and effort are much appreciated. I'll try to sort this out and will let you know should I stuck somewhere.

Answer 2

[Thorsten]

help imfreehand

[Demons]

the limit for manual interaction from human supervision is only once and this has been fulfilled when the red marker as per original image was drawn.

any automated method you could think of?

[Image Analyst]

What!?!? What kind of requirement or constraint is that? Why do you say that? Do you want to solve the problem or not? Yes, there are ways but I want to hear about these crazy constraints first. There's clearly something you haven't shared with us for some reason, and we don't want to waste time only to have you tell us "I forgot to tell you, I'm not allowed to do that either."

[Demons]

That's it really. I have to deal with thousands of those images, further manual interaction will results the same time taken to segment the whole area manually hence why the limitation.

Excuse my ignorance as I'm a beginner matlab user and dont properly know the rules of thumb as to what's the best lines to ask