mask segmentation

Question

zhao 2012-6-18

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/41395-mask-segmentation

I`m doing a femoral x-ray image precessing.According to Image Analyst`s code,I can get a binary mask.However,I need to handle hundreds of the similar femoral X-ray images.If i draw one by one,It`ll be time wasting.I think if I apply the first binary mask to the other x-ray images,it`ll be good and efficient.But I need the mask can be modified according to the different image. Can i set several adjusting point on the mask so that it can be adjusted . this is Analyst`s code

 {% 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.
%
% 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 standard MATLAB gray scale demo image.
grayImage = imread('cameraman.tif');
subplot(2, 3, 1);
imshow(grayImage, []);
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();
% Display the freehand mask.
subplot(2, 3, 2);
imshow(binaryImage);
title('Binary mask of the region', 'FontSize', fontSize);
% 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);
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);
title('Masked Outside Region', 'FontSize', fontSize);
% Calculate the mean
meanGL = mean(blackMaskedImage(binaryImage));
% Report results.
message = sprintf('Mean value within drawn area = %.3f\nNumber of pixels = %d\nArea in pixels = %.2f', ...
meanGL, numberOfPixels1, numberOfPixels2);
msgbox(message);
% Now do the same but blacken inside the region.
insideMasked = grayImage;
insideMasked(binaryImage) = 0;
subplot(2, 3, 5);
imshow(insideMasked);
title('Masked Inside Region', 'FontSize', fontSize);
% Now crop the image.
topLine = min(x);
bottomLine = max(x);
leftColumn = min(y);
rightColumn = max(y);
width = bottomLine - topLine + 1;
height = rightColumn - leftColumn + 1;
croppedImage = imcrop(blackMaskedImage, [topLine, leftColumn, width, height]);
% Display cropped image.
subplot(2, 3, 6);
imshow(croppedImage);
title('Cropped Image', 'FontSize', fontSize);}

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Answer 1

Image Analyst 2012-6-18

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/41395-mask-segmentation#answer_51059

You need to figure out another way to segment your object than manually drawing it See my File Exchange. http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A31862 There are several other methods in there, such as intensity thresholding, color segmentation, etc.

Also, look here http://iris.usc.edu/Vision-Notes/bibliography/contentsmedical.html#Medical%20Applications,%20CAT,%20MRI,%20Ultrasound,%20Heart%20Models,%20Brain%20Models for methods that others have used successfully.