How can i divide an image into sectors with MATLAB?

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Pamela 2012-10-20

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编辑： JovanS 2022-9-18

How I can divide an image into 8 equi-angular areas using as center the center of mass of the region? Then the contour points of region are identified and if the distribution of the radii of the polar coordinates of the contour points exhibit large variation for one of the eight areas the 'border' value is increased by one. This happens for all eighths until the final value is estimated that should be between 0 and 8.

Can you help me to do this with MATLAB?

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回答（3 个）

Image Analyst 2012-10-20

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Are the "contour points" the boundary points of the sectors? Do you have those? That's just simple 10th grade trigonometry. Do you have the center of mass? That's just using regionprops where the binary image is true(size(grayImage)), and you ask for 'WeightedCentroid':

measurements = regionprops(true(size(grayImage)), grayImage, 'WeightedCentroid');
centerOfMass = measurements.WeightedCentroid;

or something like that. Then use trig to send out lines from the center of mass to the border of the image. Then use poly2mask() to create a binary image which you then multiply by your gray scale image to get the masked sector.

% Mask the image.
maskedImage = bsxfun(@times, grayImage, cast(mask, class(grayImage)));

So I think that's the step by step process I think you need to follow, if I understood you correctly.

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Image Analyst 2012-10-21

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regionprops is only used to find the centroid. You don't use it to measure orientation the way it does, where it fits an ellipse to your blob. You don't want that, so get rid of all the stuff about major and minor axis lengths. You want to just look at the binary image and calculate the distances as you have done. But you need to look at the angle of each x1,y1, which is atand(y1/x1) and determine if this is in the 0-45, 45-90, 90-135, etc. sector. There will be 8 sectors each 45 degrees wide going from 0 to 360 degrees. So you'd increment the sector it's in inside your loop

theSector = ...... % some number between 1 and 8 that you figure out
% Now add the point to the list of distances you're keeping for this sector.
% First, extract distances for only this particular sector.
thisSectorsDistances = caDistances{theSector}; 
% Add the new distance.
thisSectorsDistances = [thisSectorsDistances distance];
% Now stick it back in the cell array, into the cell for this particular sector.
caDistances(theSector} = thisSectorsDistances;

Note that because the border varies, each sector can have a different number of boundary points in it. Sector 1 might have 123 coordinates in it, while sector 2 might have 210 points in it and sector 3 might have 176 points in it, etc. That's why we use a cell array.

Now when that loop ends, you have caDistances which is now complete for all points and it has 8 cells in it. So you have to put it in a loop over the 8 cells, extracting thisSectorsDistances from it and getting their standard deviation

for c = 1 : 8
  thisSectorsDistances = caDistances {c};
  stddev = std(thisSectorsDistances);
  % Determine if it's big
  if stddev > someAmount
    borderValue = 1;
  else
    borderValue = 0
end

or something like that. I don't know the exact algorithm (I don't have time to read the paper), I know only what you said.

Pamela 2012-10-25

thanks

Walter Roberson 2013-2-3

http://www.mathworks.com/matlabcentral/answers/29922-why-your-question-is-not-urgent-or-an-emergency

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Pamela 2012-10-28

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img=imread('image');
imshow(img);
I=imagesegmentation(img)
Bw = bwboundaries(I)
border=0;
caDistances = cell(secteur,1);  
for k = 1:length(Bw)
    boundary = Bw{k};
    y1=boundary(k,2);
    x1=boundary(k,1);
    distance = sqrt((x2-x1).^2+(y2-y1).^2);
    angle = atand(y1,x1)
    if (angle<=45)
        thisSectorsDistances = caDistances{1};
        thisSectorsDistances = [thisSectorsDistances distance]; 
    elseif (45<angle<=90)
        thisSectorsDistances = caDistances{2};
        thisSectorsDistances = [thisSectorsDistances distance];
    elseif (90<angle<=135)
        thisSectorsDistances = caDistances{3};
        thisSectorsDistances = [thisSectorsDistances distance];
    .
    .
    .
    elseif (315<angle<=360)
        thisSectorsDistances = caDistances{3};
        thisSectorsDistances = [thisSectorsDistances distance];
    end
end
for c = 1 : 8
thisSectorsDistances = caDistances {c};
stddev = std(thisSectorsDistances);
% Determine if it's big
if stddev > someAmount
  borderValue = 1;
else
  borderValue = 0
end

Should I transform the polar coordinates to cartesian coordinates? The statements if condition are correct?

I'm sorry for the inconvenience

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Image Analyst 2012-10-28

No. Anyway, you have both already. You have the x,y and you have the distance from the centroid. What did you set for someAmount? You need to run through it and see what the stddev for normal lesions is, and then set someAmount a little more than that.

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Pamela 2012-10-29

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编辑：Pamela 2012-10-29

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Hi,

Before runing, I first want to make sure that the method used is correct, for example: First, secteur is an undefined variable. I don't know how can I initialize it. Second, I don't know if the line used to calculate the angle is correct or not

angle = atand(y1,x1)

Concerning the coordinates, I thought to use this method. Is this correct?

x2=measurements.Centroid(1);
y2=measurements.Centroid(2);
x2=x2/numbrows;
y2=y2/numbcolumns;

and

y1=boundary(:,2);
x1=boundary(:,1);
x1 = x1 - x2;
y1 = y1 - y2;
distance = sqrt((x2-x1).^2+(y2-y1).^2)
[angle,rho] = cart2pol(x1,y1); %find the orientation of every point in the border
angle = ((angle/(pi*2)) * 360);

thanks

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JovanS 2022-9-17

编辑：JovanS 2022-9-18

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@Image Analyst, on the comments above you said that we have to "compute the angle and which of the 8 sectors it belongs in. For each of the 8 groups of distances you computed, get their standard deviation."

I tried to follow your directions but I am not sure if the line used to calculate the angle is correct or not and if the sectors are divided in the right way. I will attach a part of matlab code if you can help me @Image Analyst