Region of interest definition algorithm

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Brittney Gorman 2021-4-27

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/814840-region-of-interest-definition-algorithm

评论： Image Analyst 2021-6-26

I am trying to build an algorithm that can detect the edges of a particle. The function attached itterates radially outword from a point and detects if each location that is either a localmaximum or exceeds a threshold. As shown in the attached image this should allow the code to independently identify the edges of each low point in the data matrix.

The problem that I have with my current code is that because it iterates both radially and outward it is incredibly time consuming. Can anyone suggest something better?

I have also attached some sample data:

function [S_boarderx,S_boardery,Board_val] = shadowedge(im,shadowx,shadowy,R, dirmax, thres, PM)
    SS = im;
    [dimx, dimy] = size(SS,1,2);
    xx = shadowx;
    yy = shadowy;
    shadow_loc = sub2ind([dimx dimy], xx, yy);
    shadow_Val = SS(shadow_loc);
    thres_val = shadow_Val+shadow_Val*thres;
for t = 1:dirmax
    for j=1:R 
    
        theta = (2*pi*t)/dirmax;
        Sx = xx+round(j*cos(theta));
        Sy = yy+round(j*sin(theta));
        Sx(Sx<= 1) = 2;
        Sy(Sy<= 1) = 2;
        Sx(Sx>= dimx-1) = dimx-1;
        Sy(Sy>= dimy-1) = dimy-1;    
        val = SS(sub2ind([dimx dimy], Sx, Sy));
        
        
        if val >= thres_val 
            S_boarderx(t,:) = Sx;
            S_boardery(t,:) = Sy;
            break 
        elseif sum(sub2ind([dimx dimy],Sx,Sy) == find(islocalmax(SS,'MinProminence',PM)))...
                || sum(sub2ind([dimx dimy],Sx+1,Sy+1) == find(islocalmax(SS,'MinProminence',PM)))...
                || sum(sub2ind([dimx dimy],Sx+1,Sy) == find(islocalmax(SS,'MinProminence',PM)))...
                || sum(sub2ind([dimx dimy],Sx,Sy+1) == find(islocalmax(SS,'MinProminence',PM)))...
                || sum(sub2ind([dimx dimy],Sx-1,Sy-1) == find(islocalmax(SS,'MinProminence',PM)))...
                || sum(sub2ind([dimx dimy],Sx-1,Sy) == find(islocalmax(SS,'MinProminence',PM)))...
                || sum(sub2ind([dimx dimy],Sx,Sy-1) == find(islocalmax(SS,'MinProminence',PM)))
            S_boarderx(t,:) = Sx;
            S_boardery(t,:) = Sy;
            break
        else
            S_boarderx(t,:) = Sx;
            S_boardery(t,:) = Sy;
        end
    end
end
Board_loc = sub2ind([dimx dimy], S_boarderx(:), S_boardery(:));
Board_val = SS(Board_loc);

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Clayton Gotberg 2021-4-28

在 MATLAB Online 中打开

My apologies - I thought the edge detection was the hangup! You can use islocalmin to find the local minima in an array:

test_array = [];
localmin_cols = islocalmin(test_array); % Since islocalmin only tests one direction
localmin_rows = islocalmin(test_array.').';
% Taking advantage of the fact that a 2D local min must be a local minimum
% in both directions
local_min = localmin_cols.*(localmin_cols==localmin_rows);
% Preparing to make chart of local minima
colormat = repmat(local_min,1,1,3);
color(1,1,1:3) = [0 0.5 0.5]; % Cyan-ish - [0 0 0] is black and [1 1 1] is white
colormat = colormat.*color;
imshow(colormat) % All of the minima are highlighted against a black background
colormat(colormat==0)=1; % Changes it to white background and pinkish points
imshow(colormat)

You should be able to marry the edge detection to the minimum finder to determine which are your regions of interest.

Something else I've just remembered is the boundary tracing capability, which may be more what you're looking for than the edge detection. For example, I got this using your sample image and the below code:

bw_Image = im2bw(Image);
boundaries = bwboundaries(bw_Image);
imshow(test);
hold on
for k=1:size(boundaries,1)
    b = boundaries{k};
    plot(b(:,2),b(:,1),'g','LineWidth',3);
end

Brittney Gorman 2021-4-29

编辑：Brittney Gorman 2021-4-29

在 MATLAB Online 中打开

SAMPLE.mat

I am sorry, I think I misunderstood your question. I call it with the following command.

[S_boarderx,S_boardery,Board_val] = shadowedge(Image,shadowx,shadowy,10, 45, .5, 0);

This should take the point in the Image array at [shadowx,shadowy] and search radially outward until the threshold of 1.5*the value at [shadowx,shadowy] is reached or a local maxima.

10 = maximum radius

45 = the number of radial directions it searches

Specifically I use the function above and run it through a loop on many points within image file SS.mat

[dimx,dimy] = size(SS,1,2);
mask = zeros(dimx,dimy,size(shadow_loc,1));
for i=1:size(shadow_loc,1)
    [sboardx, sboardy, Board_val] = shadowedge(SS,shadow(i,1),shadow(i,2),10, 45, .5, 0);
    
    R = roipoly(SS,sboardy(:),sboardx(:));
    mask(:,:,i) = R;
end
smask = sum(mask,3);
smask(smask>0) = 1;