I have an image shot with an x-ray for which I want to test different segmentation algorithm (like the ones found at http://www.academia.edu/913222/segmentation_techniques)
How can I ignore the background in the calculation, i.e. how can I ignore anything that has a gray value of under 50,000 (for a 16 bit image)?
the code I'm using right now is:
clc;
clear;
[fn,pn]=uigetfile({'*.TIF','Image files'}, 'Select an image');
x = imread(fullfile(pn,fn));
T=graythresh(x);
y=im2bw(x,T);
imshow(y);
but I also want to test different segmentation techniques.
I am trying to model the future implementation of a software in order to find the best course of action and this software will ignore the "background" (I already have a succesful implementation of the otsu algorithm.
Thanks for your wisdom =).
If you want to use Otsu only on the pixel values above 50000, you can simply write
T = graythresh(x(x>50000));
Related
I am trying to extract veins using thinning algorithm. So far i did this much of code for image enhancement and its pretty much working. But when i computed binary thresholding i am not able to identify the veins from the back ground.Due to a vague output i am not able to do further processing for thinning. Can any one tell me whats wrong in this code? or is it because the threshold has to be done in some other way.
a=imread('vein.jpg');
cform = makecform('srgb2lab');
for ii = 1:3
a(:,:,ii) = medfilt2(a(:,:,ii),[5 5]);
end
lab = applycform(a,cform);
b=lab(:,:,1);
c=im2bw(b,0.2);
neg=1-c;
color=a;
r=color(:,:,1);
r(~c)= 0;
g = color(:,:,2);
g(~c)= 0;
b = color(:,:,3);
b(~c)= 0;
color = cat(3,r,g,b);
gray=rgb2gray(color);
i1=imresize(gray,[256 256],'bilinear');
i2=histeq(i1,256);
e=medfilt2(i2,[5 5]);
figure(1),imshow(e);
f=medfilt2(e,[5 5]);
figure(2),imshow(f);
thresh_level = graythresh(g);
BW = im2bw(g, thresh_level);
figure(10),imshow(BW);
greythresh uses Otsu's method, which is a good general method that works on the distribution of intensities. However it's not ideal for every situation, particularly if you've applied lots of nonlinear processing to the image first (e.g. clipping the channels).
You could try to generate your own threshold - look at the intensity distribution or, since you have them, the distribution in the colour channels and see if there's a plausible place to separate them. You could try to model it as a mixture of Gaussians (2 seems like a good number to start with, look up gmdistribution.fit) or do some other type of clustering. Is there any information in the colour channels that you could use?
If you end up the other way - with veins that are much darker but are continuous - then you could use morphological operators on the binarised image to get it back to the expected range. Perhaps this is what the thinning algorithm does.
first a little background. I'm a psychology student so my background in coding isn't on par with you guys :-)
My problem is as follow and the most important observation is that curve fitting with 2 different programs gives completly different results for my parameters, altough my graphs stay the same. The main program we have used to fit my longitudinal data is kaleidagraph and this should be seen as kinda the 'golden standard', the program I'm trying to modify is matlab.
I was trying to be smart and wrote some code (a lot at least for me) and the goal of that code was the following:
1. Taking an individual longitudinal datafile
2. curve fitting this data on a non-parametric model using lsqcurvefit
3. obtaining figures and the points where f' and f'' are zero
This all worked well (woohoo :-)) but when I started comparing the function parameters both programs generate there is a huge difference. The kaleidagraph program stays close to it's original starting values. Matlab wanders off and sometimes gets larger by a factor 1000. The graphs stay however more or less the same in both situations and both fit the data well. However it would be lovely if I would know how to make the matlab curve fitting more 'conservative' and more located near it's original starting values.
validFitPersons = true(nbValidPersons,1);
for i=1:nbValidPersons
personalData = data{validPersons(i),3};
personalData = personalData(personalData(:,1)>=minAge,:);
% Fit a specific model for all valid persons
try
opts = optimoptions(#lsqcurvefit, 'Algorithm', 'levenberg-marquardt');
[personalParams,personalRes,personalResidual] = lsqcurvefit(heightModel,initialValues,personalData(:,1),personalData(:,2),[],[],opts);
catch
x=1;
end
Above is a the part of the code i've written to fit the datafiles into a specific model.
Below is an example of a non-parametric model i use with its function parameters.
elseif strcmpi(model,'jpa2')
% y = a.*(1-1/(1+(b_1(t+e))^c_1+(b_2(t+e))^c_2+(b_3(t+e))^c_3))
heightModel = #(params,ages) abs(params(1).*(1-1./(1+(params(2).* (ages+params(8) )).^params(5) +(params(3).* (ages+params(8) )).^params(6) +(params(4) .*(ages+params(8) )).^params(7) )));
modelStrings = {'a','b1','b2','b3','c1','c2','c3','e'};
% Define initial values
if strcmpi('male',gender)
initialValues = [176.76 0.339 0.1199 0.0764 0.42287 2.818 18.52 0.4363];
else
initialValues = [161.92 0.4173 0.1354 0.090 0.540 2.87 14.281 0.3701];
end
I've tried to mimick the curve fitting process in kaleidagraph as good as possible. There I've found they use the levenberg-marquardt algorithm which I've selected. However results still vary and I don't have any more clues about how I can change this.
Some extra adjustments:
The idea for this code was the following:
I'm trying to compare different fitting models (they are designed for this purpose). So what I do is I have 5 models with different parameters and different starting values ( the second part of my code) and next I have the general curve fitting file. Since there are different models it would be interesting if I could put restrictions into how far my starting values could wander off.
Anyone any idea how this could be done?
Anybody willing to help a psychology student?
Cheers
This is a common issue when dealing with non-linear models.
If I were, you, I would try to check if you can remove some parameters from the model in order to simplify it.
If you really want to keep your solution not too far from the initial point, you can use upper bounds and lower bounds for each variable:
x = lsqcurvefit(fun,x0,xdata,ydata,lb,ub)
defines a set of lower and upper bounds on the design variables in x so that the solution is always in the range lb ≤ x ≤ ub.
Cheers
You state:
I'm trying to compare different fitting models (they are designed for
this purpose). So what I do is I have 5 models with different
parameters and different starting values ( the second part of my code)
and next I have the general curve fitting file.
You will presumably compare the statistics from fits with different models, to see whether reductions in the fitting error are unlikely to be due to chance. You may want to rely on that comparison to pick the model that not only fits your data suitably but is also simplest (which is often referred to as the principle of parsimony).
The problem is really with the model you have shown resulting in correlated parameters and therefore overfitting, as mentioned by #David. Again, this should be resolved when you compare different models and find that some do just as well (statistically speaking) even though they involve fewer parameters.
edit
To drive the point home regarding the problem with the choice of model, here are (1) results of a trial fit using simulated data (2) the correlation matrix of the parameters in graphical form:
Note that absolute values of the correlation close to 1 indicate strongly correlated parameters, which is highly undesirable. Note also that the trend in the data is practically linear over a long portion of the dataset, which implies that 2 parameters might suffice over that stretch, so using 8 parameters to describe it seems like overkill.
I am using Matlab to create a stereo image. I have followed the example shown here:
http://www.mathworks.com/help/vision/examples/stereo-image-rectification.html
The "if" loop is where I hit problems:
if status ~= 0 || isEpipoleInImage(fMatrix, size(I1)) ...
|| isEpipoleInImage(fMatrix', size(I2))
error(['Either not enough matching points were found or '...
'the epipoles are inside the images. You may need to '...
'inspect and improve the quality of detected features ',...
'and/or improve the quality of your images.']);
I've commented out the status part so I know that there is an epipole within an image. I'm not sure what sort of problem this creates. Is it a big issue, or should I just comment this part out? Also, things look good for the most part but an outlier persists after the RANSAC implementation - any help with fixing that would be very much appreciated!
for getting rid of outliers, there are 2 things you can do.
1) Change the threshold of the feature detector
2) Modify some parts of RANSAC, such as minimum number of inliers for termination and minimum acceptable error in homography
i've this photo :
and i'm trying to make Document binarization using niblack algorithm
i've implemented the simple Niblack algorithm
T = mean + K* standardDiviation
and that was it's result:
the problem is there's some parts of the image in which the window doesn't contain any objects so it detects the noise as objects and elaborates them .
i tried to apply blurring filter then global thresholding
that was the result :
which wont be solved by any other filter
i guess the only solution is preventing the algorithm from detecting global noise if the window i free from object
i'm interested to do this using niblack algorithm not using other algorithm so any suggestions ?
i tried sauvola algorithm in this paper Adaptive document image binarization J. Sauvola*, M. PietikaKinen section 3.3
it's a modified version of niblack algorithm which uses a modified equation of niblack
which returned a pretty good answers :
as well as i tried another modification of Niblack which is implemented in this paper
in the 5.5 Algorithm No. 9a: Université de Lyon, INSA, France (C. Wolf, J-M Jolion)
which returned a good results as well :
Did you look here: https://stackoverflow.com/a/9891678/105037
local_mean = imfilter(X, filt, 'symmetric');
local_std = sqrt(imfilter(X .^ 2, filt, 'symmetric'));
X_bin = X >= (local_mean + k_threshold * local_std);
I don't see many options here if you insist to use niblack. You can change the size and type of the filter, and the threshold.
BTW, it seems that your original image has colors. This information can significantly improve black text detection.
There are range of methods that can help in this situation:
Of course, you can change algorithm it self =)
Also it is possible just apply morphology filters: first you apply maximum in the window, and after - minimum. You should tune windows size to achieve a better result, see wiki.
You can choose the hardest but the better way and try to improve Niblack's scheme. It is necessary to increase Niblack's windows size if standard deviation is smaller than some fixed number (should be tuned).
i tried the niblack algorithm with k=-0.99 and windows=990 using optimisation:
Shafait – “Efficient Implementation of Local Adaptive Thresholding
Techniques Using Integral Images”, 2008
with : T = mean + K* standardDiviation; i have this result :
the implementation of algorithm is taken here
I'm working on steganography in binary image.but I have a problem in implementation in matlab.
I want to konw that,
How can I hide a message in a binary image?
And,
How can I do it in matlab with m-file or simulation?
Steganography can be done in so many different ways that you will have to define what exactly do you need if you want something specific.
In the mean time here is an example:
Encoding:
take an image and make each pixel uses only 23 out of 24 bits for colors (for example set lowest bit to 0 on the value of red color)
this will give you W x H bits for your message
put your message bit by bit into cleared pixels
Decoding
extract only bits from the lowest bit red component in the order they were put in and... that's it.
There was recently a similar question on SO with great answer from #Jacob with code example.
You can also have a look at LSB Based Steganography article on Advanced Matlab website.
Try to check image Steganography using LSB then you will be knowing how to implement. I have try that for my project.
[fn, pn, FI] = uigetfile('*.jpg*','Select the Cover Image','multiselect','on');
coln=size(fn,2);
numberfile=coln;
for i=1:numberfile
fn(i);
entirefile=fullfile(pn,fn{i});
fid=fopen(entirefile);
fclose(fid);
end
I = imread([pn,fn{i}]);
fprintf('Cover Image Loaded ... \n\n')
[fn, pn] = uigetfile('*.txt','Select Text File')
This code is just for example how to implement, i use to select multiple frames, you use according to your need.