Let's say I have the following image.
I want to consider these two blobs as two separate blobs; however, finding connected components labels them as a single component because they are touching.
I tried img = bwmorph(img, 'branchpoints'); and that does segment and erode these two blobs but that also erases blobs other blobs. For instance, in the following image, the upper left structure has been erased, but that structure should not be erased and moreover, I would like to segment that structure as two blobs that can be evidently seen.
The unfilled blob on the left has disappeared. How to get around this problem?
here's an idea, use imfill to fill holes in your blobs:
bw=imfill(im,'holes');
then do bw-im and get this:
you can take it from there...
Related
I have lots of high resolution image files that have regions of colors, basically blobs with different rgb values. I need to go through the images and for every image make a text file that contains the coordinates to one pixel in every blob. Because I have so many files the script needs to be fast. I already wrote some scala code to do the task except it only saves locations for one blob per specific RGB value, meaning if I have two blobs of the same color that are not connected it will only save one the location for the first one found. The solution to this is for each images copy the location and colors to a map and when I find a blob flood delete (flood fill except delete instead of fill) and then keep parsing on the new map. However, I think this will make run time horribly slow because I will have to go through the entire image to add it to a map before even starting the parse. Thoughts? Am I going about this all wrong?
Thanks.
I have an ellipse in the image.After segmentation i got a broken ellipse as shown .which morphological operation is used to get the perfect ellipse
Actual input file is
output obtained is
i tried imopen ,but i will lose lower ellipse like structure .how to close the upper ellipse like structure without losing lower ones
Mask i created is
i want to segment the ellipse like structure.but some of these structures are connected with rectangular like bodies.how to separate it. erode will eliminate small ellipses
If you want to reconnect something with a mathematical morphology operator, do not use an opening (it increases the gap), but a closing (imclose)! The names are explicits.
In you case, you want to reconnect something vertically cut, so use a horizontal structuring element (type segment).
And yes, you have to invert your image, black pixels representing the absence of information.
Usually, for closing gaps, you would need the close operator.
However, since most software assume active pixels are white, you would either need to invert the image, or use the open operator.
On this image, in matlab, the following works well:
imopen(I,ones(32))
This uses a square structuring element. You may want to experiment with other shapes.
Your example also looks like you moved half of the ellipse, as opposed to some process which deleted pixels in the middle. No simple morphological operation can create a perfect ellipse out of the sample image, unless you use the knowledge that multiple components can be moved to re-form the ellipse. If that is the actual case, you can scan connected components and try to match them together.
I have an image that show some random filled circles (e.g. see here). I want to change these circles to make some irregular shapes. In other words, I want to define a distribution by which I can expand the circles. Clearly, the resulted new objects will not be circles anymore, because the generated objects are expanded based on a distribution which is variable; see this new deformed circle.
I was wondering if there is any method that can do this? In my first try, I tried to use image dilation in Matlab, but I have no idea on how the dilation "distribution" should be used.
IM2 = imdilate(IM,SE)
If you want to do it using dilation, a solution could be:
Let say Im is your original image
ImResult = Same(Im)
ImClone = Clone(Im)
Randomly delete pixels in ImClone. The number of pixels to delete may be a percentage, or whatever you prefer
ImDilate = Dilate(ImClone), with the structuring element of size N
Result = Maximum(Result, ImDilate)
If you want different size of deformations, then you iterate from step 3 to 6, with different structuring element sizes.
But what you want is more an elastic deformation. You should take a look to the free form deformation (FFD).
In the shown image, I need to find the center points of the white blobs or I need to segment each white blob (to get an image which only contains that blob) from the background.
What is the efficient way to do it?
Seems this is what exactly you are looking for: Image Segmentation Tutorial ("BlobsDemo").
It contains demo to illustrate simple blob detection, measurement, and filtering. First it finds all the objects, then filters results to pick out objects of certain sizes. The basic concepts of thresholding, labeling, and regionprops are demonstrated with examples.
You need to use watershed algoritm for segmentation.
http://www.mathworks.com/help/images/ref/watershed.html
After segment cells use regionprops function.
I'd like to resize the components contained in a 3D binary image sequence without changing any of the dimensions of the sequence itself.
I'm not sure if I need to do it on a component-by-component basis, if yes, then how do I create a transform such that the resized components are re-positioned 'correctly' in the image sequence? By 'correctly', I mean with the same centre of mass as the original unprocessed components.
(If that last paragraph doesn't make sense then please ignore)
A 2D example: suppose I wanted to enlarge by 10% the white blobs in the following [295x445] image
How would you do this without making the image itself larger?
you could use the imdilate function to dilate the regions of interest. The examples in the webpage show how to use this function.