how to identify high-intensity pixel ?

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marwa za 2017-10-4

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评论： marwa za 2017-12-2

Can anyone help me to identify high-intensity regions for segmenting the tumor( image of the brain as the following figure)

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Answer 1

Image Analyst 2017-10-4

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编辑：Image Analyst 2017-10-4

Try thresholding. See attached demo.

Also see my Image Segmentation Tutorial: http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A31862&sort=downloads_desc

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Image Analyst 2017-10-18

在 MATLAB Online 中打开

Try this:

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 = 25;
grayImage=imread('2.jpg');
% Get the dimensions of the image.  
% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(grayImage);
if numberOfColorChannels > 1
  % It's not really gray scale like we expected - it's color.
  % Use weighted sum of ALL channels to create a gray scale image.
  grayImage = rgb2gray(grayImage); 
  % ALTERNATE METHOD: Convert it to gray scale by taking only the green channel,
  % which in a typical snapshot will be the least noisy channel.
  % grayImage = grayImage(:, :, 2); % Take green channel.
end
% Display the image.
subplot(2,2,1);
imshow(grayImage);
title('Original Gray Scale Image', 'FontSize', fontSize);
% Display the histogram so we can see what gray level we need to threshold it at.
subplot(2,2,3:4);
hObj = histogram(grayImage, 256)
grid on;
title('Histogram', 'FontSize', fontSize, 'Interpreter', 'None')
% Find threshold.
counts = hObj.Values;
counts(1:50) = 0;
[maxValue, indexOfMax] = max(counts);
counts(1:indexOfMax) = maxValue;
[minValue, threshold] = min(counts)
hold on;
line([threshold, threshold], ylim, 'Color', 'r', 'LineWidth', 2);
xticks(0:20:255);
% Binarize image
binaryImage = grayImage > threshold;
subplot(2,2,2);
imshow(binaryImage);
title('Binary Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.96]);

Image Analyst 2017-10-31

在 MATLAB Online 中打开

Threshold the skullFreeImage instead of the original gray scale image. Then use the mask to erase everything from the image except the tumor.

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 = 20;
grayImage=imread('2.jpg');
[rows, columns, numberOfColorChannels] = size(grayImage);
if numberOfColorChannels > 1
  grayImage = rgb2gray(grayImage);   
end
%%%%%%%%%%
subplot(2,3,1);
imshow(grayImage);
title('Original Grayscale Image', 'FontSize', fontSize);
% Display the histogram so we can see what gray level we need to threshold it at.
subplot(2, 3, 2:3);
hObj = histogram(grayImage, 256)
grid on;
title('Histogram', 'FontSize', fontSize, 'Interpreter', 'None')
% Find threshold.
counts = hObj.Values;
counts(1:50) = 0;
[maxValue, indexOfMax] = max(counts);
counts(1:indexOfMax) = maxValue;
[minValue, threshold] = min(counts)
hold on;
line([threshold, threshold], ylim, 'Color', 'r', 'LineWidth', 2);
%xticks(0:20:255);
% Binarize image
binaryImage = grayImage > threshold;
subplot(2,3,4);
imshow(binaryImage);
title('Binary Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.96]);
%%%%%%%%%%%
% Extract the outer blob, which is the skull.  
% The outermost blob will have a label number of 1.
labeledImage = bwlabel(binaryImage);    % Assign label ID numbers to all blobs.
binaryImage = ismember(labeledImage, 1);  % Use ismember() to extract blob #1.
% Thicken it a little with imdilate().
binaryImage = imdilate(binaryImage, true(5));
% Display the final binary image.
subplot(2, 3, 5);
imshow(binaryImage, []);
axis on;
caption = sprintf('Extraction Skull ');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
%%%%%%%%%%%
% Mask out the skull from the original gray scale image.
skullFreeImage = grayImage; % Initialize
skullFreeImage(binaryImage) = 0; % Mask out.
% Display the image.
subplot(2, 3, 6);
imshow(skullFreeImage, []);
axis on;
caption = sprintf('Gray Scale Image\nwith Skull Stripped Away');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
%%%%%%%%%%%
% Give user a chance to see the results on this figure, then offer to continue and find the tumor.
promptMessage = sprintf('Do you want to continue and find the tumor,\nor Quit?');
titleBarCaption = 'Continue?';
buttonText = questdlg(promptMessage, titleBarCaption, 'Continue', 'Quit', 'Continue');
if strcmpi(buttonText, 'Quit')
  return;
end
% Now threshold to find the tumor
binaryImage = skullFreeImage > threshold;
% Display the image.
hFig2 = figure();
subplot(2, 2, 1);
imshow(binaryImage, []);
axis on;
caption = sprintf('Initial Binary Image\nThresholded at %d Gray Levels', threshold);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
%%%%%%%%%%%
% Assume the tumor is the largest blob, so extract it
%binaryTumorImage = bwareafilt(binaryImage, 1);
binaryTumorImage = bwareaopen(binaryImage, 100);
% Display the image.
subplot(2, 2, 2);
imshow(binaryTumorImage, []);
axis on;
caption = sprintf('Tumor Alone (binary mask)');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
%%%%%%%%%%%
% Find tumor boundaries.
% bwboundaries() returns a cell array, where each cell contains the row/column coordinates for an object in the image.
% Plot the borders of the tumor over the original grayscale image using the coordinates returned by bwboundaries.
subplot(2, 2, 3);
imshow(grayImage, []);
axis on;
caption = sprintf('Tumor\nOutlined in red in the overlay'); 
title(caption, 'FontSize', fontSize,'Interpreter', 'None'); 
axis image; % Make sure image is not artificially stretched because of screen's aspect ratio.
hold on;
boundaries = bwboundaries(binaryTumorImage);
numberOfBoundaries = size(boundaries, 1);
for k = 1 : numberOfBoundaries
  thisBoundary = boundaries{k};
  % Note: since array is row, column not x,y to get the x you need to use the second column of thisBoundary.
  plot(thisBoundary(:,2), thisBoundary(:,1), 'r', 'LineWidth', 2);
end
hold off;
% Extract the gray scale tumors alone
tumorOnlyImage = grayImage; % Initialize
% Erase everything except the tumors
tumorOnlyImage(~binaryTumorImage) = 0;
subplot(2, 2, 4);
imshow(tumorOnlyImage, []);
axis on;
caption = sprintf('Tumor only (gray scale image)'); 
title(caption, 'FontSize', fontSize,'Interpreter', 'None'); 
axis image; % Make sure image is not artificially stretched because of screen's aspect ratio.

Image Analyst 2017-12-2

That's why you need to use more sophisticated, and complicated, algorithms.

marwa za 2017-12-2

Yes, it's true

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how to identify high-intensity pixel ?

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how to identify high-intensity pixel ?

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