I am new to matlab. Actually I have to do retinal blood vessels segmentation. I have used kmeans clustering for segmentation, but result is not satisfactory. Now I want to try out fuzzy c means clustering technique. However I am not able to find out how to use matlab built in function for this purpose. Please guide me about this. I have gone through the following page, but I am not able to understand how to apply all this to my image.
https://cn.mathworks.com/help/fuzzy/fcm.html
Thanks
A minimal working example:
% some sample rgb image
MyImage = imread('autumn.tif');
% display it
figure; imshow(MyImage)
% size of the image
sz = size(MyImage);
% reshape the image to column format (each color band into one column). I guess you
%also did this for the k-means. If not that's why you did get poor results.
ImageInColumnFormat = reshape(MyImage,[],sz(3));
% number of clusters you want
NumberOfClusters = 4;
% U shows how likely each pixel belongs to each cluster.
% double() is only necessary because the sample image is uint8 and fcm has trouble with that format. You may not have to do that.
[~,U] = fcm(double(ImageInColumnFormat),NumberOfClusters);
% Get for each pixel the most likely cluster
[~,Labels] = max(U,[],1);
% reshape it back into the image format
LabelsInImageFormat = reshape(Labels,sz(1),sz(2));
% show result
figure; imagesc(LabelsInImageFormat)
Related
I have a problem when binarizing an image:
The walker in the picture is lost after binarizing. Could anyone offer help? Here's the code I used:
clc;
clear;
video = VideoReader('C:\Users\Small_Bird\Desktop\ch02_20170323193606~2.avi');
nFrames = video.NumberOfFrames;
H = video.Height;
W = video.Width;
Rate = video.Preallocate movie structure.
for frameNum = 3500:nFrames
P = read(video,frameNum);
grayImage=rgb2gray(P);
cannyEdge=edge(grayImage,'canny');
[m,n]=size(grayImage);
for i=1:m
for j=1:n
if 1==cannyEdge(i,j)
h(i,j)=grayImage(i,j)+3;
else
h(i,j)=grayImage(i,j);
end
end
end
thresh=graythresh(h);
I2=im2bw(h,thresh);
subplot(2,2,1);
imshow(grayImage),title('original image');
subplot(2,2,2);
imshow(cannyEdge),title('image after extracting edge');
subplot(2,2,3);
imshow(h),title('image after strengthening edge');
subplot(2,2,4);
imshow(I2),title('image after binaryzation');
end
The issue is the choice of threshold for im2bw. You're using the function graythresh to compute a global threshold across the whole image, which your results show only succeeds in separating the black parts of the image from the gray-or-higher parts of the image. You'll need to choose a higher threshold, either an absolute one you use for all images or one computed from some features of each image.
If you have MATLAB version R2016a or newer you have options for computing a locally adaptive threshold using either adaptthresh or im2binarize (the replacement for the im2bw function in newer versions) using the 'adaptive' method. This may give you better results than a simple global threshold.
I have an image with multivariate Gaussian distribution in histogram. I want to segment the image to two regions so that they both can follow the normal distribution like the red and blue curves shows in histogram. I know Gaussian mixture model potentially works for that. I tried to use fitgmdist function and then clustering the two parts but still not work well. Any suggestion will be appreciated.
Below is the Matlab code for my appraoch.
% Read Image
I = imread('demo.png');
I = rgb2gray(I);
data = I(:);
% Fit a gaussian mixture model
obj = fitgmdist(data,2);
idx = cluster(obj,data);
cluster1 = data(idx == 1,:);
cluster2 = data(idx == 2,:);
% Display Histogram
histogram(cluster1)
histogram(cluster2)
Your solution is correct
The way you are displaying your histogram poorly represents the detected distributions.
Normalize the bin sizes because histogram is a frequency count
Make the axes limits consistent (or plot on same axis)
These two small changes show that you're actually getting a pretty good distribution fit.
histogram(cluster1,0:.01:1); hold on;
histogram(cluster2,0:.01:1);
Re-fit a gaussian-curve to each cluster
Once you have your clusters if you treat them as independent distributions, you can smooth the tails where the two distributions merge.
gcluster1 = fitdist(cluster1,'Normal');
gcluster2 = fitdist(cluster2,'Normal');
x_values = 0:.01:1;
y1 = pdf(gcluster1,x_values);
y2 = pdf(gcluster2,x_values);
plot(x_values,y1);hold on;
plot(x_values,y2);
How are you trying to use this 'model'? If the data is constant, then why dont you measure, the mean/variances for the two gaussians seperately?
And if you are trying to generate new values from this mixed distribution, then you can look into a mixture model with weights given to each of the above distributions.
I am trying to convert the rgb image into a grayscale and then cluster it using kmean function of matlab .
here is my code
he = imread('tumor2.jpg');
%convert into a grayscale image
ab=rgb2gray(he);
nrows = size(ab,1);
ncols = size(ab,2);
%convert the image into a column vector
ab = reshape(ab,nrows*ncols,1);
%nColors=no of clusters
nColors = 3;
%cluster_idx is a n x 1 vector where cluster_idx(i) is the index of cluster assigned to ith pixel
[cluster_idx, cluster_center ,cluster_sum] = kmeans(ab,nColors,'distance','sqEuclidean','Replicates',1,'EmptyAction','drop' );
figure;
%converting vector into a matrix of dimensions equal to that of original
%image dimensions (nrows x ncols)
pixel_labels = reshape(cluster_idx,nrows,ncols);
pixel_labels
imshow(pixel_labels,[]), title('image labeled by cluster index');
problems
1) output image is always a plain white image.
i tried the solution given in the link below but output of the image is a plain gray image in this case.
find the solution tried here
2) when i execute my code second time ,execution does not proceed beyond k-mean function (it is likes an infinite loop there). hence no output in this case.
Actually, it looks like when you are colour segmenting kmeans is known to fall in local minima. This means that often, it wont find the amount of clusters you want as the minimization is not the best (that's why lots of people use other type of segmentation, such as level sets or simple region growing).
An option is to increase the amount of Replicates (amount of times kmeans will try to find the answer). At the moment you are setting it to 1, but you could try 3 or 4, and it may reach the solution that way.
In this question the accepted answer recommends to use a kmeans version of the algorithm specifically created for image segmentation. I havent tried myself but I think its worth a shot.
Link to FEX
I have 2 images im1 and im2 shown below. Theim2 picture is the same as im1, but the only difference between them is the colors. im1 has RGB ranges of (0-255, 0-255, 0-255) for each color channel while im2 has RGB ranges of (201-255, 126-255, 140-255). My exercise is to reverse the added effects so I can restore im2 to im1 as closely as I can. I have 2 thoughts in mind. The first is to match their histograms so they both have the same colors. I tried it using histeq but it restores only a portion of the image. Is there any way to change im2's histogram to be exactly the same as im1? The second approach was just to copy each pixel value from im1 to im2 but this is wrong since it doesn't restore the original image state. Are there any suggestions to restore the image?
#sepdek below pretty much suggested the method that #NKN alluded to, but I will provide another approach. One more alternative I can suggest is to perform a colour correction based on a least mean squared solution. What this alludes to is that we can assume that transforming a pixel from im2 to im1 requires a linear combination of weights. In other words, given a RGB pixel where its red, green and blue components are shaped into a 3 x 1 vector from the corrupted image (im2), there exists some linear transformation to get its equivalent pixel in the clean image (im1). In other words, we have this relationship:
[R_im1] [R_im2]
[G_im1] = A * [G_im2]
[B_im1] [B_im2]
Y = A * X
A in this case would be a 3 x 3 matrix. This is essentially performing a matrix multiplication to get your output corrected pixel. The input RGB pixel from im2 would be X and the output RGB pixel from im1 would be Y. We can extend this to as many pixels as we want, where pairs of pixels from im1 and im2 would establish columns along Y and X. In general, this would further extend X and Y to 3 x N matrices. To find the matrix A, you would find the least mean squared error solution. I won't get into it, but to find the optimal matrix of A, this requires finding the pseudo-inverse. In our case here, A would thus equal to:
Once you find this matrix A, you would need to take each pixel in your image, shape it so that it becomes a 3 x 1 vector, then multiply A with this vector like the approach above. One thing you're probably asking yourself is what kinds of pixels do I need to grab from both images to make the above approach work? One guideline you must adhere to is that you need to make sure that you're sampling from the same spatial location between the two images. As such, if we were to grab a pixel at... say... row 4, column 9, you need to make sure that both pixels from im1 and im2 come from this same row and same column, and they are placed in the same corresponding columns in X and Y.
Another small caveat with this approach is that you need to be sure that you sample a lot of pixels in the image to get a good solution, and you also need to make sure the spread of your sampling is over the entire image. If we localize the sampling to be within a small area, then you're not getting a good enough distribution of the colours and so the output will not look very nice. It's up to you on how many pixels you choose for the problem, but from experience, you get to a point where the output starts to plateau and you don't see any difference. For demonstration purposes, I chose 2000 pixels in random positions throughout the image.
As such, this is what the code would look like. I use randperm to generate a random permutation from 1 to M where M is the total number of pixels in the image. These generate linear indices so that we can sample from the images and construct our matrices. We then apply the above equation to find A, then take each pixel and apply a matrix multiplication with A to get the output. Without further ado:
close all;
clear all;
im1 = imread('http://i.stack.imgur.com/GtgHU.jpg');
im2 = imread('http://i.stack.imgur.com/wHW50.jpg');
rng(123); %// Set seed for reproducibility
num_colours = 2000;
ind = randperm(numel(im1) / size(im1,3), num_colours);
%// Grab colours from original image
red_out = im1(:,:,1);
green_out = im1(:,:,2);
blue_out = im1(:,:,3);
%// Grab colours from corrupted image
red_in = im2(:,:,1);
green_in = im2(:,:,2);
blue_in = im2(:,:,3);
%// Create 3 x N matrices
X = double([red_in(ind); green_in(ind); blue_in(ind)]);
Y = double([red_out(ind); green_out(ind); blue_out(ind)]);
%// Find A
A = Y*(X.')/(X*X.');
%// Cast im2 to double for precision
im2_double = double(im2);
%// Apply matrix multiplication
out = cast(reshape((A*reshape(permute(im2_double, [3 1 2]), 3, [])).', ...
[size(im2_double,1) size(im2_double,2), 3]), class(im2));
Let's go through this code slowly. I am reading your images directly from StackOverflow. After, I use rng to set the seed so that you can reproduce the same results on your end. Setting the seed is useful because it allows you to reproduce the random pixel selection that I did. We generate those linear indices, then create our 3 x N matrices for both im1 and im2. Finding A is exactly how I described, but you're probably not used to the rdivide / / operator. rdivide finds the inverse on the right side of the operator, then multiplies it with whatever is on the left side. This is a more efficient way of doing the calculation, rather than calculating the inverse of the right side separately, then multiplying with the left when you're done. In fact, MATLAB will give you a warning stating to avoid calculating the inverse separately and that you should the divide operators instead. Next, I cast im2 to double to ensure precision as A will most likely be floating point valued, then go through the multiplication of each pixel with A to compute the result. That last line of code looks pretty intimidating, but if you want to figure out how I derived this, I used this to create vintage style photos which also require a matrix multiplication much like this approach and you can read up about it here: How do I create vintage images in MATLAB? . out stores our final image. After running this code and showing what out looks like, this is what we get:
Now, the output looks completely scrambled, but the colour distribution more or less mimics what the input original image looks like. I have a few explanations on why this is the case:
There is quantization noise. If you take a look at the final image, there is various white spotting all over. This is probably due to the quantization error that is introduced when compressing your image. Pixels that should map to the same colours between the images will have slight variations due to quantization which gives us that spotting
There is more than one colour from im2 that maps to im1. If there is more than one colour from im2 that maps to im1, it is impossible for a linear multiplication with the matrix A to be able to generate more than one kind of colour for im1 given a single pixel in im2. Instead, the least mean-squared solution will try and generate a colour that minimizes the error and give you the best colour possible instead. This is probably way the face and other fine details of the image are obscured because of this exact reason.
The image is noisy. Your im2 is not completely clean. I can also see various spots of salt and pepper noise across all of the channels. One bad thing about this method is that if your image is subject to noise, then this method will not faithfully reconstruct the original image properly. Your image can only be corrupted by a wrong mapping of colours. Should there be any other type of image noise introduced, then this method will definitely not work as you are trying to reconstruct the original image based on a noisy image. There are pixels in the noisy image that were never present in the original image, so you'll have no luck getting it back to the way it was before!
If you want to take a look at the histograms of each channel between the original image and the output image, this is what we get:
The code I used to generate the above figure was:
names = {'Red', 'Green', 'Blue'};
figure;
for idx = 1 : 3
subplot(3,2,2*idx - 1);
imhist(im1(:,:,idx));
title([names{idx} ': Image 1']);
end
for idx = 1 : 3
subplot(3,2,2*idx);
imhist(out(:,:,idx));
title([names{idx} ': Output']);
end
The left side shows the red, green and blue histograms for the original image while the right side shows the same histograms for the reconstructed image. You can see that the general shape more or less mimics the original image, but there are some spikes throughout - most likely attributed to quantization noise and the non-unique mapping between colours of both images.
All in all, this is the best that I could do, but I think that was the whole point of the exercise.... to show that it isn't possible.
For more information on how to perform colour correction, check out Richard Alan Peters' II Digital Image Processing slides on colour correction. This was what I started with, and the derivation of how to calculate A can be found in his slides. Perhaps you can use some of what he talks about in your future work.
Good luck!
It seems that you need a scaling function to map the values of im2 to the values of im1.
This is fairly simple and you could write a scaling function to have it available for any such case.
A basic scaling mapping would work as follows:
out_value = min_output + (in_value - min_input) * (outrange / inrange)
given that there is an input value in_value that is within a range of values inrange=max_input-min_input and the mapping results an output value out_value within a range outrange=max_output-min_output. We also need to take into account the minimum input and output range bounds (min_input and min_output) to have a correct mapping.
See for example the following code for a scaling function:
%
% scale the values of a matrix using a set of limits
% possible ways to use:
% y = scale( x, in_range, out_range) --> ex. y = scale( x, [8 230], [0 255])
% y = scale( x, out_range) --> ex. y = scale( x, [0 1])
%
function y = scale( x, varargin );
if nargin<2,
error([upper(mfilename),':: Syntax: y=',mfilename,'(x[,in_range],out_range)']);
end;
if nargin==2,
inrange=[min(x(:)) max(x(:))]; % compute the limits of the input variable
outrange=varargin{1}; % get the output limits from the arguments
else
inrange=varargin{1}; % get the input limits from the arguments
outrange=varargin{2}; % get the output limits from the arguments
end;
if diff(inrange)==0, % row or column vector matrix or scalar
% just do a clipping...
if x>=outrange(2),
y=outrange(2);
elseif x<=outrange(1),
y=outrange(1);
else
y=x;
end;
else
% actually scale the data
% using: out = min_output + (x-min_input) * (outrange / inrange)
y = outrange(1) + (x-inrange(1))*abs(diff(outrange))/abs(diff(inrange));
end;
This function gets a matrix of values and scales them to a desired range.
In your case it could be used as following (variable img is the scaled im2):
for i=1:size(im1,3), % for each of the input/output image channels
output_range = [min(min(im1(:,:,i))) max(max(im1(:,:,i)))];
img(:,:,i) = scale( im2(:,:,i), output_range);
end;
This way im2 is scaled to the range of values of im1 one channel at a time. Output variable img should be the desired one.
My problem is that I have a radar image in png format. (Sorry, but i had to remove the image as my colleague says it a copyright infringement of the German Weather Service)
I want to read the image in MATLAB. Then read all the clouds, and label each cloud with a unique index. This means that each pixel belonging to a certain cloud is labeled with the same index i. Calculate the center of area(coa) of each cloud and then I should be able to measure distances between clouds from one coa to another.
Some similar work I know was done in IDL. I tried using that but it would be much easier for me if I'm able to do all this in MATLAB and concentrate more on the result, rather then spend time learning IDL.
So, before jumping in, I want to know if all this is possible in MATLAB. If yes, can you guide me a little on how I can extract the cloud and label them?
First do some basic image analysis such as thresholding or median filtering and so forth, to reduce noise if relevant.
Then you can use bwlabel to label each cloud with a unique index. The use reigonprops to find the centroids.
Here's a very basic code sample:
d=imread('u09q8.png');
bw = im2bw(d,0.1); % thereshold at 50%
bw = bwareaopen(bw, 10); % Remove objects smaller than 10 pixels from binary image
bw=bwlabel(bw); % label each cloud
stats=regionprops(bw,'Centroid'); % find centroid coordinates of all labeled clouds
Yes it is possible.
Regarding the cloud detection, it is a step by step process.
It will be based on the algorithm you are going to use. You can start here.
Yes, of course. This can be done using k-means clustering.
You can read about imread and kmeans. The example given in the official documentation of kmeans shows exactly what you need.
For example, if you want to cluster your image into 5 clouds:
%// Read the image
I = imread('clouds.png');
[y, x] = find(I); %// Obtain all coordinates
y = size(I, 1) - y + 1; %// Adjust y-coordinates
K = 5;
[idx, c] = kmeans([x, y], K); %// Classify clouds into K clusters
Now idx stores the corresponding cluster indices and c stores the coordinates of the centroids.
To draw the results, you can do something like this:
%// Plot results
figure, hold on
scatter(x, y, 5, idx) %// Plot the clusters
plot(c(:, 1), c(:, 2), 'r+') %// Plot centroids
text(c(:, 1) + 10, c(:, 2), num2str((1:K)'), 'color', 'r') %// Plot cluster IDs
Note that this method requires predetermining the number of clusters K in advance. Alternatively, you can use this tool to attempt to automatically detect the number of clusters.
EDIT: Due to the copyright claim I removed the resulting image.