I have a matlab script for 8-bit image analysis and I'm trying to enhance objects in the image by subtracting the background with no objects present. What I want to do at a pixel level is:
if B-I>50 then E=I
else E=255-B-I
Where, B is the background, I the image and E my enhanced image. I know I can do this by looping through each element of the image matrix by the following:
diff=imsubtract(B,I);
nrows=1024;
ncols=1360;
for r=1:nrows
for c=1:ncols
if diff(r,c)>50
E=I(r,c);
else
E=255-diff(r,c);
end
end
end
But is this rather slow when going multiple images. I've also tried the follow:
E=255-diff;
E(diff>50)=I;
But receive the following error:
In an assignment A(I) = B, the
number of elements in B and I must
be the same.
Any tips on optimizing this would be greatly apprenticed!
In an assignment A(I) = B, the number of elements in B and I must be
the same.
The reason for this error is that you are trying to assign all the content of I to a subset of E (those pixels where diff>50). You have to specifically tell MATLAB that you want those pixels set to the matching pixels in I.
E(diff>50)=I(diff>50);
Incidentally you should be careful using imsubtract here. For pixels where I has a higher value than B, that will result in zeros (if your values are uint8). It may be okay (not 100% clear if you're looking for the absolute difference or really just where B is larger than I)
What if you use use find()
ind = find(B-I>50)
E(ind) = I(ind)
% And then the ones that are not `B-I>50`
E(~ind) = 255-B(~ind)-I(~ind)
Try this vectorized approach that uses logical indexing. I could not test it out on images though, so would be great if that's taken care of.
Code
diff1=double(imsubtract(B,I));
E = double(I).*(diff1>50) + (255-diff1).*(diff1<=50);
You might be needed to convert the datatype back to unsigned integer formats as used for images.
Related
I created binarized images by using the Otsu methode in Matlab and cut out parts of the resulting image using a function. Now i want to take a look at these images with the VolumeViewer command. I know the x,y and z dimensions of the resulting imgages. I currently run this code doing it(excluding the volumeViewerwhich happens after the loop):
files= {'C3\C3_000mal_550_539_527.raw';...
};
for i=1:numel(files)
Image = fopen(files{i},'r');
ImageData{i} = fread(Image,Inf,'uint16=>uint16');
ImageData{i} = reshape(ImageData{i},550,539,[]);
fclose(openedCrystalImage);
end
Using this code runs into the following error using reshape:
Error using reshape
Product of known dimensions, 296450, not divisible into total number of elements, 78114575.
I did the maths and 550*539=296450 and 296450 * 527=156229150: If we divide the last number by the number of elements it equals 2 and thus is divisible into the total number of elements. In my opinion the reshape function is not able to find the size of the last dimension or defines it as 1.
Defining the size of z also results in an error suggesting using the brackets [], so the function can find it.
Error using reshape
Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size
for that dimension.
Now to the weird part. This code works for another set of images, with diffrent sizes of the x,y and z ranges. So don´t know where the issue lies to be frank. So i would really appreciate and Answer to my question
I figured it out. The error lies here:
ImageData{i} = fread(Image,Inf,'uint16=>uint16');
Apparently by saving them as .raw before it converts the image to an 8 bit file rather than 16 bits it had before. Therefore, my dimension is double the size of the number of elements. With this alteration it works:
ImageData{i} = fread(Image,Inf,'uint8=>uint8');
The reason i was able to look at the other pictures was that the z range was divisble by 2.
So the reshape function was not the problem but size of the integer data while creating the array for the variable ImageData.
P.S. I just started out programming so the accuracy in the answer should be taken with a grain of salt
I have a dataset of n nifti (.nii) images. Ideally, I'd like to be able to get the value of the same voxel/element from each image, and apply a function to the n data points. I'd like to do this for each voxel/element across the whole image, so that I can reconvert the result back into .nii format.
I've used the Tools for NIfTI and ANALYZE image toolbox to load my images:
data(1)=load_nii('C:\file1.nii');
data(2)=load_nii('C:\file2.nii');
...
data(n)=load_nii('C:\filen.nii');
From which I obtain a struct object with each sub-field containing one loaded nifti. Each of these has a subfield 'img' corresponding to the image data I want to work on. The problem comes from trying to select a given xyz within each img field of data(1) to data(n). As I discovered, it isn't possible to select in this way:
data(:).img(x,y,z)
or
data(1:n).img(x,y,z)
because matlab doesn't support it. The contents of the first brackets have to be scalar for the call to work. The solution from googling around seems to be a loop that creates a temporary variable:
for z = 1:nz
for x = 1:nx
for y = 1:ny
for i=1:n;
points(i)=data(i).img(x,y,z);
end
[p1(x,y,z,:),~,p2(x,y,z)] = fit_data(a,points,b);
end
end
end
which works, but takes too long (several days) for a single set of images given the size of nx, ny, nz (several hundred each).
I've been looking for a solution to speed up the code, which I believe depends on removing those loops by vectorisation, preselecting the img fields (via getfield ?)and concatenating them, and applying something like arrayfun/cellfun/structfun, but i'm frankly a bit lost on how to do it. I can only think of ways to pre-select which themselves require loops, which seems to defeat the purpose of the exercise (though a solution with fewer loops, or fewer nested loops at least, might do it), or fun into the same problem that calls like data(:).img(x,y,z) dont work. googling around again is throwing up ways to select and concatenate fields within a struct, or a given field across multiple structs. But I can't find anything for my problem: select an element from a non-scalar sub-field in a sub-struct of a struct object (with the minimum of loops). Finally I need the output to be in the form of a matrix that the toolbox above can turn back into a nifti.
Any and all suggestions, clues, hints and help greatly appreciated!
You can concatenate images as a 4D array and use linear indexes to speed up calculations:
img = cat(4,data.img);
p1 = zeros(nx,ny,nz,n);
p2 = zeros(nx,ny,nz);
sz = ny*nx*nz;
for k = 1 : sz
points = img(k:sz:end);
[p1(k:sz:end),~,p2(k)] = fit_data(a,points,b);
end
I am trying to sum in the second dimension a matrix QI in Matlab. The trick is, the columns contain a series of increasing numbers, but not all columns have the same number of elements (i.e. numel(QI(:,1)) ~= numel(QI(:,2)) and so on). For the sake of clarity, I attach a picture of it. Note that I padded the missing areas with 0, so the previous condition becomes nnz(QI(:,1)) ~= nnz(QI(:,2)).
One initial strategy that I thought of was to treat this as an image and construct a mask for each different gradient level, but that seems like a tedious job.
Anyone has a better idea on how to do this? I should also mention that I am able to freely modify how QI is generated, but I'd rather not if there is a solution for this problem.
EDIT:
Hopefully the new colored image should give a better understanding.
FYI, each column was previously stored in a cell array without the trailing zeros. Then I extracted the columns one by one and stored them in a matrix in order to perform the summation, padding the extra zeros whenever the length isn't the same.
Generally these column data should have the same number of rows, but sometimes that's not the case, and even worse, they do not allign properly.
I'm starting to think if it's better to rework the code that generate the cell arrays rather than this matrix. Thoughts?
Thank you,
edit: following you comment, I modified the answer. Be aware that your data cannot be really "aligned" because they have not the same number of value.
A way would be to use a cell as a storage for your measures.
valueMissing = 0; % here you can put the defauld value you want
% transform you matrix in a cell
QICell = arrayfun(#(x) QI(QI(:,x)!=valueMissing,x), 1:size(QI,2),'UniformOutput', false);
Now you can sum the last element of the vectors inside the cell
QIsum = sum(cellfun(#(x) x(end), QICell))
Or reorder the vectors so that your last element are "aligned"
QICellReordered = cellfun(#(x) x(end:-1:1),QICell, 'UniformOutput',false);
Then you can make all possible sums:
m = min(cellfun(#numel, QICellReordered));
QIsum = zeros(m,1);
for i=1:m
QIsum(i) = sum(cellfun(#(x) x(i), QICellReordered));
end
% reorder QISum to your original order
QIsum = QIsum(end:-1:1);
I hope this help !
Given a BW image that contains some connected components.
Then, given a single pixel P in the image. How to find which component that contains the pixel P? It is guaranteed that the pixel P is always on the white area in one of the connected components.
Currently, I use CC = bwconncomp(BW) than I iterate each component using 'for' loop. In the each component, I iterate the index pixel. For each pixels, I check whether the value equal to the (index of) pixel P or not. If I find it, I record the number of connected component.
However, it seems it is not efficient for this simple task. Any suggestion for improvement? Thank you very much in advance.
MATLAB provides multiple functions that implement connected-component in different ways.
In your example, I would suggest bwlabel.
http://www.mathworks.com/help/images/ref/bwlabel.html
[L, num] = bwlabel(imgBW) This will perform a full-image connected-component labeling on a black-and-white image.
After calling this function, the label value that pixel P belongs to can be read off the result matrix L, as in label_to_find = L(row, col) index. Simple as that.
To extract a mask image for that label, use logical(L == label_to_find).
If you use different software packages such as OpenCV you will be able to get better performance (efficiency in terms of cutting unnecessary or redundant computation), but in MATLAB the emphasis is on convenience and prototyping speed.
I'm attempting to run this simple diffusion case (I understand that it isn't ideal generally), and I'm doing fine with getting the inside of the solid, but need some help with the outer edges.
global M
size=100
M=zeros(size,size);
M(25,25)=50;
for diffusive_steps=1:500
oldM=M;
newM=zeros(size,size);
for i=2:size-1;
for j=2:size-1;
%we're considering the ij-th pixel
pixel_conc=oldM(i,j);
newM(i,j+1)=newM(i,j+1)+pixel_conc/4;
newM(i,j-1)=newM(i,j-1)+pixel_conc/4;
newM(i+1,j)=newM(i+1,j)+pixel_conc/4;
newM(i-1,j)=newM(i-1,j)+pixel_conc/4;
end
end
M=newM;
end
It's a pretty simple piece of code, and I know that. I'm not very good at using Octave yet (chemist by trade), so I'd appreciate any help!
If you have concerns about the border of your simulation you could pad your matrix with NaN values, and then remove the border after the simulation has completed. NaN stands for not a number and is often used to denote blank data. There are many MATLAB functions work in a useful way with these values.
e.g. finding the mean of an array which has blanks:
nanmean([0 nan 5 nan 10])
ans =
5
In your case, I would start by adding a border of NaNs to your M matrix. I'm using 'n' instead of 'size', since size is an important function in MATLAB, and using it as a variable can lead to confusing errors.
n=100;
blankM=zeros(n+2,n+2);
blankM([1,end],:) = nan;
blankM(:, [1,end]) = nan;
Now we can define 'M'. N.B that the first column and row will be NaNs so we need to add an offset (25+1):
M = blankM;
M(26,26)=50;
Run the simulation through,
m = size(blankM, 1);
n = size(blankM, 2);
for diffusive_steps=1:500
oldM = M;
newM = blankM;
for i=2:m-1;
for j=2:n-1;
pixel_conc=oldM(i,j);
newM(i,j+1)=newM(i,j+1)+pixel_conc/4;
newM(i,j-1)=newM(i,j-1)+pixel_conc/4;
newM(i+1,j)=newM(i+1,j)+pixel_conc/4;
newM(i-1,j)=newM(i-1,j)+pixel_conc/4;
end
end
M=newM;
end
and then extract the area of interest
finalResult = M(2:end-1, 2:end-1);
One simple change you might make is to add a boundary of ghost cells, or halo, around the domain of interest. Rather than mis-use the name size I've used a variable called sz. Replace:
M=zeros(sz,sz)
with
M=zeros(sz+2,sz+2)
and then compute your diffusion over the interior of this augmented matrix, ie over cells (2:sz+1,2:sz+1). When it comes to considering the results, discard or just ignore the halo.
Even simpler would be to simply take what you already have and ignore the cells in your existing matrix which are on the N,S,E,W edges.
This technique is widely used in problems such as, and similar to, yours and avoids the need to write code which deals with the computations on cells which don't have a full complement of neighbours. Setting the appropriate value for the contents of the halo cells is a problem-dependent matter, 0 isn't always the right value.