I have an image with some regions that I would like to join, would this be possible?
The image is the following:
I'm using regionprops to count that regions as well, and I want that the result of this image is 2 regions instead of 4 that actually are, more or less like that:
(this image is an example, in order to explain it better).
In fact, I want to join the regions that are near each other.
Would this be possible? How?
I would first convolve
(conv2 -- https://www.mathworks.com/help/matlab/ref/conv2.html)
the original image with a 2D Gaussian in order to smooth the image. In doing so, the edges of the regions will be broadened and run into one another -- effectively making multiple regions that are close together bleed into one region. You will have to play with the 2D Gaussian by varying your sigma in order to achieve the desired smoothness.
Once the image is filtered/smoothed, you can use your original algorithm within regionprops to count the number or regions.
Let me know if that helps or if I am being unclear.
Related
I have two binary images, each of which have a single white filled parallelogram and a black background. The only difference between the two images is that the parallelograms are in different locations and are slightly different from one another in shape. All the parameters between the two images are the same except for that one change.
I want to check how similar the shape of the two parallelograms are, by using some sort of comparing measure.
I looked into ssimval function in MATLAB but it seems to be taking the whole image into consideration rather than just the white blobs. Is there any other function I can use for this purpose?
For visually checking similarity, you can plot their probability density function and for numeric similarity, compute some similarity measure, such as, KL Divergence, etc.
In a simple way, you can segment your binary image with simple bwlabel function. Then use regionprops function to find perimeter and area of your desire segment. Moreover, center of region is also another comparison point.
You could do it with polygons, by using the polyshape class.
First convert the binary mask to a set of corner points. You can do it with a convex hull, by calling regionprops(bwI, 'ConvexHull').
Then convert the corner points into polygons, by calling polyshape.
Finally measure the dissimiliarities of the polygons by measuring their turning distance. Turning distance is rotation- and scaling invariant, so you might want to add additive terms to your distance metric for those if your problem demands it.
A very simple solution for comparing two binary image is using boolean operations.
Your images contains zero and one values. so If you use boolean operation.
suppose your two images are : B1 , B2
C = B1 & (~B2)
if sum(C(:))==0
% two image are same
else
% two image are different
end
I was wondering if anyone knows which kind of filter is applied by SPCImage from the Becker & Hickl system.
I am taking some FLIM data with my system and I want to create the lifetime images. For doing so I want to bin my images in the same way as it does SPCImage, so I can increase my SN ratio. The binning goes like 1x1, 3x3, 5x5, etc. I have created the function for doing a 3x3 binning, but each time it gets more complicated...
I want to do it in MATLAB, and maybe there is already a function that can help me with this.
Many thanks for your help.
This question is old, but for anyone else wondering: You want to sum the pixels in an (2M+1) x (2M+1) neighborhood for each plane (M integer). So I am pretty sure you can go about the problem by treating it like a convolution.
#This is your original 3D SDT image
#I assume that you have ordered the image with spatial dimensions along the
#first and second and the time channels are the third dimension.
img = ... #<- your 3D image goes here
#This describes your filter. M=1 means take 1 a one pixel rect around your
#center pixel and add the values to your center, etc... (i.e. M=1 equals a
#total of 3x3 pixels accumulated)
M=2
#this is the (2D) filter for your convolution
filtr = ones(2*M+1, 2*M+1);
#the resulting binned image (3D)
img_binned = convn(img, filtr, 'same');
You should definitely check the result against your calculation, but it should do the trick.
I think that you need to test/investigate image filter functions to apply to this king of images Fluorescence-lifetime imaging microscopy.
A median filter as showing here is good for smoothering things. Or a weihgted moving average filter where applied to the image erase de bright spots and only are maintained the broad features
So you need to review of the digital image processing in matlab
I am struggling to find a good contour detection function that would count the number of contour in bw images that I have processed using some previous tools. As you can see, my profile picture is an example of such images,
,
In this image, ideally, I wish to have a function which counts four closed contour.
I don't mind if it also detects the really tiny contours in between, or the entire shape itself as extra contours. As long as it counts the medium sized ones, I can fix the rest by applying area threshold. My problem is that any function I have tried detects only one contour - the entire shape, as it cannot separate it to the su-conours which are connected to one another.
Any suggestions?
Here is my shot at this, although your question might get closed because it's off-topic, too broad or a possible duplicate. Anyhow I propose another way to count the number of contours. You could also do it using bwboundaries as was demonstrated in the link provided by #knedlsepp in the possible duplicate. Just for the sake of it here is another way.
The idea is to apply a morphological closure of your image and actually count the number of enclosed surfaces instead instead of contours. That might end up being the same thing but I think it's easier to visualize surfaces.
Since the shapes in your image look like circle (kind of...) the structuring element used to close the image is a disk. The size (here 5) is up to you but for the image you provided its fine. After that, use regionprops to locate image regions (here the blobs) and count them, which comes back to counting contours I guess. You can provide the Area parameter to filter out shapes based on their area. Here I ask the function to provide centroids to plot them.
Whole code:
clear
clc
close all
%// Read, threshold and clean up the image
Im = im2bw(imread('ImContour.png'));
Im = imclearborder(Im);
%// Apply disk structuring element to morphologically close the image.
%// Play around with the size to alter the output.
se = strel('disk',5);
Im_closed = imclose(Im,se);
%// Find centroids of circle-ish shapes. Youcan also get the area to filter
%// out those you don't want.
S = regionprops(~Im_closed,'Centroid','Area');
%// remove the outer border of the image (1st output of regioprops).
S(1) = [];
%// Make array with centroids and show them.
Centro = vertcat(S.Centroid);
imshow(Im)
hold on
scatter(Centro(:,1),Centro(:,2),40,'filled')
And the output:
So as you see the algorithm detected 5 regions, but try playing a bit with the parameters and you will see which ones to change to get the desired output of 4.
Have fun!
I have an image like this:
What I want to do is to find the outer edge of this cell and the inner edge in the cell between the two parts of different colors.
But this image contains to much detail I think, and is there any way to simplify this image, remove those small edges and find the edges I want?
I have tried the edge function provided by matlab. But it can only find the outer edge and disturbed by those detailed edges.
This is a very challenging work due to the ambiguous boundaries and tiny difference between red and green intensities. If you want to implement the segmentation very precisely and meet some medical requirements, Shai's k-means plus graph cuts may be one of the very few options (EM algorithm may be an alternative). If you have a large database that has many similar images, some machine learning methods might help. Otherwise, I just wrote a very simple code to roughly extract the internal red region for you. The boundary is not that accurate since some of the green regions are also included.
I1=I;
I=rgb2hsv(I);
I=I(:,:,1); % the channel with relatively large margin between green and red
I=I.*(I<0.25);
I=imdilate(I, true(5));
% I=imfill(I,'holes'); depends on what is your definition of the inner boundary
bw=bwconncomp(I);
ar=bw.PixelIdxList;
% find the largest labeled area,
n=0;
for i=1:length(ar)
if length(ar{i})>n
n=length(ar{i});
num=i;
end
end
bw1=bwlabel(I);
bwfinal(:,:,1)=(bw1==num).*double(I1(:,:,1));
bwfinal(:,:,2)=(bw1==num).*double(I1(:,:,2));
bwfinal(:,:,3)=(bw1==num).*double(I1(:,:,3));
bwfinal=uint8(bwfinal);
imshow(bwfinal)
It seems to me you have three dominant colors in the image:
1. blue-ish background (but also present inside cell as "noise")
2. grenn-ish one part of cell
3. red-ish - second part of cell
If these three colors are distinct enough, you may try and segment the image using k-means and Graph cuts.
First stage - use k-means to associate each pixels with one of three dominant colors. Apply k-means to the colors of the image (each pixel is a 3-vector in your chosen color space). Run k-means with k=3, keep for each pixel its distance to centroids.
Second stage - separate cell from background. Do a binary segmentation using graph-cut. The data cost for each pixel is either the distance to the background color (if pixel is labeled "background"), or the minimal distance to the other two colors (if pixel is labeled "foreground"). Use image contrast to set the pair-wise weights for the smoothness term.
Third stage - separate the two parts of the cell. Again do a binary segmentation using graph-cut but this time work only on pixels marked as "cell" in the previous stage. The data term for pixels that the k-means assigned to background but are labeled as cell should be zero for all labels (these are the "noise" pixels inside the cell).
You may find my matlab wrapper for graph-cuts useful for this task.
I am looking for a method that looks for shapes in 3D image in matlab. I don't have a real 3D sample image right now; in fact, my 3D image is actually a set of quantized 2D images.
The figure below is what I am trying to accomplish:
Although the example figure above is a 2D image, please understand that I am trying to do this in 3D. The input shape has these "tentacles", and I have to look for irregular shapes among them. The size of the tentacle from one point to another can change around but at "consistent and smooth" pace - that is it can be big at first, then gradually smaller later. But if suddenly, the shape just gets bigger not so gradually, like the red bottom right area in the figure above, then this is one of the volume of interests. Note that these shapes have more tendency to be rounded and spherical, but some of them are completely arbitrary and random.
I've tried the following methods so far:
Erode n times and dilate n times: given that the "tentacles" are always smaller than the volume of interest, this method will work as long as the volume is not too small. And, we need to have a mechanism to deal with thicker portion of the tentacle that becomes false positive somehow.
Hough Transform: although I have been suggested this method earlier (from Segmenting circle-like shapes out of Binary Image), I see that it works for some of the more rounded shape cases, but at the same time, more difficult cases such that of less-rounded, distorted, and/or arbitrary shapes can slip through this method.
Isosurface: because of my input is a set of 2D quantized images, using an isosurface allow me to reconstruct image in 3D and see things clearer. However, I'm not sure what could be done further in this case.
So can anyone suggests some other techniques for segmenting such shape out of these "tentacles"?
Every point on your image has the property that it is either part of the tentacle, or part of the volume of interest. If it is unknown apriori what the expected girth of the tentacle is, then 1 wont work because we won't be able to set n. However, we know that the n that erases the tentacle is smaller than the n that erases the node. You can for each point replace it with an integer representing the distance to the edge. Effectively, this can be done via successive single pixel erosion, and replacing each pixel with the count of the iteration at which it was erased. Lets call this the thickness at the pixel, but my rusty old mind tells me that there was a term of art for this.
Now we want to search for regions that have a higher-than-typical morphological distance from the boundary. I would do this by first skeletonizing the image (http://www.mathworks.com/help/toolbox/images/ref/bwmorph.html) and then searching for local maxima of the thickness along the skeleton. These are points on the skeleton where the thickness is larger than the neighbor points.
Finally I would sort the local maxima by the thickness, a threshold on which should help to separate the volumes of interest from the false positives.