斑点的集群生长

Question

考虑以下来自 Mathworks 的图像：

我用

标记了斑点

 [L, num]= bwlabel(I);

我如何迭代地连接所有的斑点，i.e.start一个斑点并找到最接近it.Consider最左边的两个斑点的一个，可以从中绘制很多条线一个 blob 的许多点连接到另一个 blob，但是最短的点将通过找到一个 blob 的最接近另一个 blob 的像素来获得，在另一个 blob 中找到一个相似的像素并连接这两个 pixels.I 想在这个 manner.After 中连接它们，连接它们，这使它们成为一个单一的斑点，找到最接近这个新斑点的斑点连接它们，依此类推，直到整个图像都有一个单一的封闭结构？另外，斑点并不总是圆形的，它们的形状是随机的。

这里有人问过类似的问题：

How to find the shortest path between two blobs(contours/closed curves) using MATLAB?
和 http://in.mathworks.com/matlabcentral/newsreader/view_thread/270149

使用 bwdist()，我可以分离两个斑点并使用蛮力方法通过测试第二个 link 中提到的两个斑点中的所有像素对来找到最短距离，但它需要真的很长 time.Is 有更好的方法来解决这个问题以便更快地获得结果吗？

编辑：

这是另一张图片：

所需图片：

Answer 1

方法 #1：连接质心点

%// Read image, convert to binary and remove some whitish border across it
im = im2bw(imread('http://i.stack.imgur.com/vUsrl.png'));
BW = im(3:end-2,3:end-2);
figure, imshow(BW), title('Starting/Original Image')

%// Find centroid points for each blob
cpts = reshape(round(struct2array(regionprops(BW,'Centroid'))),2,[])'; %//'

%// Initialize 2 groups- "hungry" & "feeder" groups, naming them as grp1 & grp2
grp1 = []; grp2 = cpts;

%// Initialize the blob index matching IDs
R = 1; C = 1;

while ~isempty(grp2)
    
    %// Get one from Group-2 into Group 1 based on the closest one that was
    %//obtained from the previous iteration. Remove that from Group -2.
    grp1 = [grp1 ; grp2(C,:)];
    grp2(C,:) = [];
    
    %// Find squared distances between those two groups
    sq_distmat = squared_dist(grp1,grp2);
    
    %// Find the IDs minimum one across row and column which would be the
    %IDs for group 1 and 2 respectively, calling them as R and C
    [~,idx] = min(sq_distmat(:));
    [R,C] = ind2sub(size(sq_distmat),idx);
    
    %// Draw the connecting line
    BW = linept(BW, grp1(R,2), grp1(R,1), grp2(C,2), grp2(C,1));
    
end
figure, imshow(BW), title('Final Connected Image')

关联函数-

function sq_distmat = squared_dist(A,B)

[nA,dim] = size(A);
nB = size(B,1);

A_ext = ones(nA,dim*3);
A_ext(:,2:3:end) = -2*A;
A_ext(:,3:3:end) = A.^2;

B_ext = ones(nB,dim*3);
B_ext(:,1:3:end) = B.^2;
B_ext(:,2:3:end) = B;

sq_distmat = A_ext * B_ext.';

return;

动画乐趣-

方法 #2：连接轮廓点

%// Read image, convert to binary and remove some whitish border across it
im = im2bw(imread('http://i.stack.imgur.com/vUsrl.png'));
BW = im(3:end-2,3:end-2);

%// Find boundary points as a cell array
bpts_cell = bwboundaries(BW);

%// Initialize 2 groups- "hungry" & "feeder" groups, naming them as grp1 & grp2
grp1c = []; grp2c = bpts_cell;

ID = 1;
for iter = 1:numel(bpts_cell)-1
    
    %// Get one from Group-2 into Group 1 based on the closest one that was
    %obtained from the previous iteration. Remove that from Group -2.
    grp1c = [grp1c ; grp2c(ID)];
    grp2c(ID,:) = [];
    grp1 = vertcat(grp1c{:});
    grp2 = vertcat(grp2c{:});
    
    %// Find squared distances between those two groups
    sq_distmat = squared_dist(grp1,grp2);
    
    %// Find the IDs minimum one across row and column which would be the
    %IDs for group 1 and 2 respectively, calling them as R and C
    [~,idx] = min(sq_distmat(:));
    [R,C] = ind2sub(size(sq_distmat),idx);
    
    %// Draw the connecting line
    BW = linept(BW, grp1(R,1), grp1(R,2), grp2(C,1), grp2(C,2));
    
    lens = cellfun('length',grp2c);
    clens = cumsum(lens);    
    ID = find(C<=clens,1);
end

动画乐趣-

使用编辑图像的动画输出 -

斑点的集群生长

Cluster growing of blobs

performance

matlab

image-processing

shortest-path

方法 #1：连接质心点

方法 #2：连接轮廓点