I'm studying weed optimization algorithm of brain MRI images using Matlab R2017a. I obtained sample code from the Matlab File Exchange of Weed Optimization Algorithm. When I rand code with a binary image, I got an error message:
Undefined function 'colon' for input arguments of type 'uint8' and attributes 'full 3d real'.
What should I do to solve this problem?
This problem doesn't occur for some images. Sometimes, I get a matrix dimension error. Despite all errors, sample code works great 100%.
I=imread('brainxx.jpeg');
x=graytresh(I);
w=im2bw(I,x);
for.....
%min pixel value
%max pixel value
%image area[pixels]
.....
within-class variance (sum of weighted bg and fg variance)
% lowest within-class variance = optimal threshold (t)
%find and set the optimal threshold and generate binary image
[dat,indx]=min(var_tot);
opt_td = im2double(opt_t)...
Related
I've been running a variation of the doseResponse function downloaded from here to generate dose-response sigmoid curves. However, I've had trouble with one of my datasets generating a linear curve instead. By running the following code, I get the following error and produce the following graph. I also uploaded the data called dose2.csv and resp2.csv to google drive here. Does anyone know how I can fix this? Thanks.
Code to generate graph
% Plotting Dose-Response Curve
response = resp2;
dose = dose2;
% Deal with 0 dosage by using it to normalise the results.
normalised=0;
if (sum(dose(:)==0)>0)
%compute mean control response
controlResponse=mean(response(dose==0));
%remove controls from dose/response curve
response=response(dose~=0)/controlResponse;
dose=dose(dose~=0);
normalised=1;
end
%hill equation sigmoid
sigmoid=#(beta,x)beta(1)+(beta(2)-beta(1))./(1+(x/beta(3)).^beta(4));
%calculate some rough guesses for initial parameters
minResponse=min(response);
maxResponse=max(response);
midResponse=mean([minResponse maxResponse]);
minDose=min(dose);
maxDose=max(dose);
%fit the curve and compute the values
%[coeffs,r,J]=nlinfit(dose,response,sigmoid,[minResponse maxResponse midResponse 1]); % nlinfit doesn't work as well
beta_new = lsqcurvefit(sigmoid,[minResponse maxResponse midResponse 1],dose,response);
[coeffs,r,J]=nlinfit(dose,response,sigmoid, beta_new);
ec50=coeffs(3);
hillCoeff=coeffs(4);
%plot the fitted sigmoid
xpoints=logspace(log10(minDose),log10(maxDose),1000);
semilogx(xpoints,sigmoid(coeffs,xpoints),'Color',[1 0 0],'LineWidth',2)
hold on
%notate the EC50
text(ec50,mean([coeffs(1) coeffs(2)]),[' \leftarrow ' sprintf('EC_{50}=%0.2g',ec50)],'FontSize',20,'Color',[1 0 0]);
%plot mean response for each dose with standard error
doses=unique(dose);
meanResponse=zeros(1,length(doses));
stdErrResponse=zeros(1,length(doses));
for i=1:length(doses)
responses=response(dose==doses(i));
meanResponse(i)=mean(responses);
stdErrResponse(i)=std(responses)/sqrt(length(responses));
%stdErrResponse(i)=std(responses);
end
errorbar(doses,meanResponse,stdErrResponse,'o','Color',[1 0 0],'LineWidth',2,'MarkerSize',12)
Warning Message
Solver stopped prematurely.
lsqcurvefit stopped because it exceeded the function evaluation limit,
options.MaxFunctionEvaluations = 4.000000e+02.
Warning: Iteration limit exceeded. Returning results from final iteration.
Graph (looking to generate a sigmoid curve not linear)
You also need to optimize your initial value [minResponse maxResponse midResponse 1] for lsqcurvefit. Don't just simply start with minimum or maximum values of given values. Instead, you may first start with your equations to estimate your coefficients.
Given the sigmoid model of sigmoid=#(beta,x)beta(1)+(beta(2)-beta(1))./(1+(x/beta(3)).^beta(4)). As x gets arbitrarily close to inf, equation will return beta(2). And as x gets arbitrarily close to 0, equation will return beta(1). Therefore, initial estimation of minResponse, maxResponse, and midResponse seems reasonable enough. Actually your problem lies in your initial estimation of 1. beta(4) can be roughly estimated with the inclination of your log graph. To my rough sketch it was around 1/4 and therefore you may conclude that your initial estimation of 1 was too large for convergence.
beta_new = lsqcurvefit(sigmoid,[minResponse maxResponse midResponse 1/4],dose,response);
I am working on pedestrian step detection algorithm (acceleration data), I need to calculate statistical features from my filtered signal not raw data. I have already calculated mean, var and std and now I want to calculate correlation coefficients and gradient from filtered data. My filter data is of 1x37205 double. I calculated these features using for loop with moving window size=2samples and 50% overlap of previous window. Below I am attaching the code I tried. But when I run this for corrcoef it gives 1's as output for the whole data and for gradient it gives error Assignment has more non-singleton rhs dimensions than non-singleton subscripts. I am unable to understand well. Could some one suggest me or provide any code help in matlab and/or how can I work on that?
windowsize=2;
%%C is used for corrcoef and G for gradient. %%Data_filtered is caclulated
%%from acceleration xyz.
function [C, G] = features(Data_filtered, window)
C=zeros(length(Data_filtered),1);
G=zeros(length(Data_filtered),1);
for i=window:(length(Data_filtered))
C(i,1)=corrcoef(Data_filtered(i+1-window:i));
end;
for i=window:(length(Data_filtered))
G(i,1)=gradient(Data_filtered(i+1-window:i));
end;
end
I'm trying to port some Matlab code to C++.
I've come across this line:
edges = edge(gray,'canny',0.1);
The output for the sample image is a completely black image. I want to reproduce the same behaviour using cv::Canny. What values should I use for low threshold and high threshold?
Sample:
Output:
In the line above you have not defined a threshold, probably it takes zero then, thus delivering a black picture. Also, you use a sigma of 0.1 which means virtually no Gauss Blur in the first Canny step. Within Matlab you can get an optimized threhold by:
[~, th] = edge(gray,'canny');
and then apply the optimized threshold th multiplied by some factor f (from my experience f should be between 1-3), you have to try out:
edges=edge(gray,'canny',f*th,'both', sigma);
sigma is sqrt(2) by default (you used 0.1 above). Following remarks:
Matlab calculated the optimized threshold as a percentile of the distribution of intensity gradients (you can see the construction of edge() if you enter "edit edge", if I remember correctly)
the above parameter th is a vector consisting of the low and high threshold. Matlab always uses low_threshold = 0.4* high_threshold
I have an RGB image and I am trying to calculate its Gaussian derivative.
Image is a greyscale image and the Gaussian window is 5x5,st is the standard deviation
This is the code i am using in order to find a 2D Gaussian derivative,in Matlab:
N=2
[X,Y]=meshgrid(-N:N,-N:N)
G=exp(-(x.^2+y.^2)/(2*st^2))/(2*pi*st)
G_x = -x.*G/(st^2);
G_x_s = G_x/sum(G_x(:));
G_y = -y.*G/(st^2);
G_y_s = G_y/sum(G_y(:));
where st is the standard deviation i am using. Before I proceed to the convolution of the Image using G_x_s and G_y_s, i have the following problem. When I use a standard deviation that is an even number(2,4,6,8) the program works and gives results as expected. But when i use an odd number for standard deviation (3 or 5) then the G_y_s value becomes Inf because sum(G_y(:))=0. I do not understand that behavior and I was wondering if there is some problem with the code or if in the above formula the standard deviation can only be an even number. Any help will be greatly appreciated.
Thank you.
Your program doesn't work at all. The results you find when using an even number is just because of some numerical errors.
Your G will be a matrix symmetrical to the center. x and y are both point symmetrical to the center. So the multiplication (G times x or y) will result in a matrix with a sum of zero. So a division by that sum is a division by zero. Everything else you observe is because of some roundoff errors. Here, I see a sum og G_xof about 1.3e-17.
I think your error is in the multiplication x.*G and y.*G. I can not figure out why you would do that.
I assume you want to do edge detection rigth? You can use fspecialto create several edge filters. Laplace of gaussian for instance. You could also create two gaussian filters with different standard deviations and subtract them from another to get an edge filter.
I was trying to find the entropy of a certain probability distribution in MATLAB. For p, I tried doing
E = -sum(p .* log2(p))
and Echeck = entropy(p)
Shouldn't E and Echeck be same?
The matlab help on entropy does say Entropy is defined as -sum(p.*log2(p))
where p contains the histogram counts returned from imhist.But also that entropy converts any class other than logical to uint8 for the histogram count calculation since it is actually trying to calculate the entropy of a grayscale image and hence wants the pixel values to be discrete.
So I guess it's incorrect to use this function for my purpose?
Is there a good alternative?
I used open entropy to check the code, and there is a line:
if ~islogical(I)
I = im2uint8(I);
end
p = imhist(I(:));
which mean that the input is converted to uint8, and then the function computes the entropy of the histogram of the input, and not of the input itself.
That explains the difference.