How to close boundary of a circular object in binary image

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CW Hsu
CW Hsu am 29 Nov. 2022
Kommentiert: CW Hsu am 9 Dez. 2022
Thank you all for being here.
I have an image below
the outer boundary is not closed. So it would be a problem when I calculate area of this bubble.
How can I close the boundary of circular object in image above image automatically?

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Matt J
Matt J am 29 Nov. 2022
load Image
[c,r]=imfindcircles(BW,[10,100]);
area=pi*r^2
area = 1.1299e+03
  5 Kommentare
Matt J
Matt J am 1 Dez. 2022
Bearbeitet: Matt J am 1 Dez. 2022
Use a loop over the different detected circle data c(i,:) and r(i) to repeat the process for many objects.
CW Hsu
CW Hsu am 9 Dez. 2022
Thanks for your help. It works successfully

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Weitere Antworten (1)

Image Analyst
Image Analyst am 30 Nov. 2022
Try this, where mask is your binary image
mask = bwconvhull(mask, 'union');
props = regionprops(mask, 'Area', 'EquivDiameter');
area = props.Area
diameter = props.EquivDiameter
  2 Kommentare
CW Hsu
CW Hsu am 1 Dez. 2022
@Image Analyst Thanks for your advice.
Since I have many images, usually there is not only one circular object in the image. Can I use 'union'? Or maybe 'object' ?
But for the image I show at the top. I try using 'object' . The image becomes image like this.
Image Analyst
Image Analyst am 1 Dez. 2022
You can use the 'objects' option but keep in mind that if the curves are too far apart it will consider them as two separate blobs. So two C shapes might end up as two solid D shapes. Only if their convex hulls overlap would they be considered a single blob. And then you might get something like your bottom image. So you'd have to call bwconvhull a second time to get a convex shape.
Another option would be to use dbscan to identify which pixels are close to each other. You specify that closeness distance. So this essentially labels each group of blobs with a single ID number. Then you can use ismember to extract each group one at a time, and then use the 'union' option since all pixels in the image now belong to a single group. Then repeat for all groups in the image. I'm attaching a demo I created of dbscan. See Wikipedia if you don't know what dbscan is.

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