MATLAB: Identifying and marking image regions

bwboundariesimage processingImage Processing Toolboximdilate

Hello all,

I have an image (original.png) that I need to separate regions where there are and are not these white dots. The final image should look something like (final.png).

I've tried playing with some image processing functions but because the spaces between the dots is so large relative to the size of the dots I can't find an effective way of filling in the spaces.

Original.png

Final.png

Best Answer

There are at least four ways that I can think of. One is to to an imclose() with a really big kernel - big enough so that the dots connect. Then use bwboundaries to find the "envelope."

The second way is to use alpha shapes: http://cgm.cs.mcgill.ca/~godfried/teaching/projects97/belair/alpha.html. The File Exchange has submissions on alpha shapes, as well as snakes, active contours, balloons, or whatever you want to call them.

The third way is to use a "restricted" convex hull - something I've been after the Mathworks for years to add. I have a inefficient demo of that if you're interested. Basically it's kind of like the convex hull but it only looks within a certain distance to find convex points, so it has the effects of going into the little bays, nooks, and crannies more. So you'll get something like you showed in the red line, whereas a standard convex hull is just like you wrapped a rubber band around the points so that you will have no following of the points into bays.

The fourth way might be to calculate the Euclidean Distance transform with bwdist() and then threshold. I haven't tried this (just thought it up now) but it might work.

Related Solutions

MATLAB: How to find convex hull and the area of intersection

Sum the intersection image to find the area. Try this code:

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
imtool close all;  % Close all imtool figures.
clear;  % Erase all existing variables.
workspace;  % Make sure the workspace panel is showing.
fontSize = 20;
P(1,:) = [0.9 2.1]; 
P(2,:) = [2.8 5.2]; 
P(3,:) = [5.7 2.3];
windowSize = 30;
image1 = poly2mask(P(:,1), P(:,2), windowSize, windowSize);
subplot(2, 2,1);
imshow(image1);
title('P', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]); % Maximize figure.
set(gcf,'name','Demo by ImageAnalyst','numbertitle','off') 
Q = [2.7 1.1; 5.6 4.1; 8.0 1.8];
image2 = poly2mask(Q(:,1), Q(:,2), windowSize, windowSize);
subplot(2, 2, 2);
imshow(image2);
title('Q', 'FontSize', fontSize);
intersectionImage = image1 | image2;
subplot(2, 2, 3);
imshow(intersectionImage);
title('Intersection Image', 'FontSize', fontSize);
intersectionCoords = [P;Q];
convexHullIndex = convhull(intersectionCoords)
convexHullImage = poly2mask(intersectionCoords(convexHullIndex,1), intersectionCoords(convexHullIndex,2), windowSize, windowSize);
subplot(2, 2, 4);
imshow(convexHullImage);
title('Convex Hull Image', 'FontSize', fontSize);

MATLAB: Finding tortuosity of parasite

Use bwskel() to get the longest spine. Then use bwmorph() to get the endpoints of the spine. Then use find() and sqrt() to get the straight line distance between the endpoints, then divide the two.

% This demo computes the tortuosity of a snake-like shape by dividing the actual longest spine distance by the straight line distance between the endpoints.
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 15;
%--------------------------------------------------------------------------------------------------------

%    READ IN IMAGE
folder = pwd;
baseFileName = 'worm.png';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
	% The file doesn't exist -- didn't find it there in that folder.
	% Check the entire search path (other folders) for the file by stripping off the folder.
	fullFileNameOnSearchPath = baseFileName; % No path this time.
	if ~exist(fullFileNameOnSearchPath, '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
% Read in the image from disk.  If storedColorMap is not empty, it's an indexed image with a stored colormap.
[grayImage, storedColorMap] = imread(fullFileName);
if ~isempty(storedColorMap)
	grayImage = ind2rgb(grayImage, storedColorMap);
end
% 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.
hFig = figure;
subplot(1, 2, 1);
imshow(grayImage, []);
title('Original Grayscale Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;
%--------------------------------------------------------------------------------------------------------
%    SEGMENTATION
% Binarize the image
binaryImage = imbinarize(grayImage);
% Take the largest blob only.
binaryImage = bwareafilt(binaryImage, 1);
% Fill holes, if any.
binaryImage = imfill(binaryImage, 'holes');
% Display the binary image.
subplot(1, 2, 1);
imshow(binaryImage, []);
title('Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis('on', 'image');
% Now skeletonize
skelImage = bwskel(binaryImage); % First guess.  Will have short spurs.
% First get the area of the skeleton and take half of it to be the longest spine length.
% This will get rid of all spurs.
MinBranchLength = round(sum(skelImage(:))/2)
% Now take the skeleton again using that min branch length we just computed.
skelImage = bwskel(binaryImage, 'MinBranchLength', MinBranchLength);
% Display the skeleton image.
subplot(1, 2, 2);
imshow(skelImage, []);
title('Skeleton Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis('on', 'image');
% Find the endpoints
endpointImage = bwmorph(skelImage, 'endpoints');
[rows, columns] = find(endpointImage)
% Draw a red line between them.
hold on;
plot(columns, rows, 'r-', 'LineWidth', 2);
% Find the spine length.
% (Would be more accurate to find the spine length by traversing it.)
spineLength = sum(skelImage(:))
% Compute the straight line distance.
straightLineDistance = sqrt((columns(2) - columns(1))^2 + (rows(2) - rows(1))^2)
% Compute the tortuosity
tortuosity = spineLength / straightLineDistance
caption = sprintf('Skeleton Image.  Tortuosity = %f', tortuosity);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');

Best Answer

Related Solutions

MATLAB: How to find convex hull and the area of intersection

MATLAB: Finding tortuosity of parasite

Related Question