I have a set of roughly 2000 images to process, and have stumbled on a problem regarding my edges. The original images are CT Scans of a heart, which are then thresholded and sobel filtered to produce a binary image of parts of the tissue. Unfortunately the thresholding and filtering has resulted in certain images missing the 'edges' of the heart --> R&LHS you can see the gaps & further down Just a small one at the top hopefully this shows how annoying this is to do automatically
I've tried various inbuilt methods of edge detection, but the difference in size of the gaps makes it nearly impossible to do, without filling out the bits that are supposed to remain as gaps due to the sizes of the filters I use.
Is there a way of manually "connecting the dots" so to speak? It would take forever on the whole image set but seems to be my only option, or if you could suggest any other way of doing it would be cool!
I have 42 variables and I have calculated the correlation matrix for them in Matlab. Now I would like to visualize it with a schemaball. Does anyone have any suggestions / experiences how this could be done in Matlab? The following pictures will explain my point better:
In the pictures each parabola between variables would mean the strength of correlation between them. The thicker the line is, the more correlation. I prefer the style of picture 1 more than the style in picture 2 where I have used different colors to highlight the strength of correlation.
Kinda finished I guess.. code can be found here at github.
Documentation is included in the file.
The yellow/magenta color (for positive/negative correlation) is configurable, as well as the fontsize of the labels and the angles at which the labels are plotted, so you can get fancy if you want and not distribute them evenly along the perimeter/group some/...
If you want to actually print these graphs or use them outside matlab, I suggest using vector formats (eg eps). It's also annoying that the text resizes when you zoom in/out, but I don't know of any way to fix that without hacking the zoom function :/
schemaball % demo
schemaball(arrayfun(#num2str,1:10,'uni',false), rand(10).^8,11,[0.1587 0.8750],[0.8333 1],2*pi*sin(linspace(0,pi/2-pi/20,10)))
schemaball(arrayfun(#num2str,1:50,'uni',false), rand(50).^50,9)
I finished and submitted my version to the FEX: schemaball and will update the link asap.
There are a some differences with Gunther Struyf's contribution:
You can return the handles to the graphic object for full manual customization
Labels are oriented to allow maximum left-to-rigth readability
The figure stretches to fit labels in, leaving the axes unchanged
Syntax requires only correlations matrix (but allows optional inputs)
Optimized for performance.
Follow examples of demo, custom labels and creative customization.
Note: the first figure was exported with saveas(), all others with export_fig.
schemaball
x = rand(10).^3;
x(:,3) = 1.3*mean(x,2);
schemaball(x, {'Hi','how','is','your','day?', 'Do','you','like','schemaballs?','NO!!'})
h = schemaball;
set(h.l(~isnan(h.l)), 'LineWidth',1.2)
set(h.s, 'MarkerEdgeColor','red','LineWidth',2,'SizeData',100)
set(h.t, 'EdgeColor','white','LineWidth',1)
The default colormap:
To improve on screen rendering you can launch MATLAB with the experimental -hgVersion 2 switch which produces anti/aliased graphics by default now (source: HG2 update | Undocumented Matlab). However, if you try to save the figure, the file will have the usual old anti-aliased rendering, so here's a printscreen image of Gunther's schemaball:
Important update:
You can do this in Matlab now with the FileExchange submission:
http://www.mathworks.com/matlabcentral/fileexchange/48576-circulargraph
There is an exmample by Matlab in here:
http://uk.mathworks.com/examples/matlab/3859-circular-graph-examples
Which gives this kind of beautiful plots:
Coincidentally, Cleve Moler (MathWorks Chief Mathematician) showed an example of just this sort of plot on his most recent blog post (not nearly as beautiful as the ones in your example, and the connecting lines are straight rather than parabolic, but it looks functional). Unfortunately he didn't include the code directly, but if you leave him a comment on the post he's usually very willing to share things.
What might be even nicer for you is that he also applies (and this time includes) code to permute the rows/columns of the array in order to maximize the spatial proximity of highly connected nodes, rather than randomly ordering them around the circumference. You end up with a 'crescent'-shaped envelope of connecting lines, with the thick bit of the crescent representing the most highly connected nodes.
Unfortunately however, I suspect that if you need to enhance his code to get the very narrow, high-resolution lines in your example plots, then MATLAB's currently non-anti-aliased graphics aren't quite up to it yet.
I've recently been experimenting with MATLAB data and the D3 visualization library for similar graphs - there are several related types of circular visualizations you may be interested in and many of them are interactive. Another helpful, well-baked, and freely available option is Circos which is probably responsible for most of the prettier versions of these graphs you've seen in popular press.
I'm a newbie to Matlab. I'm basically attempting to manually segment a set of images and then manually label those segments also. I looked into the imfreehand(), but I'm unable to do this using imfreehand().
Basically, I want to follow the following steps :
Manually segment various ROIs on the image (imfreehand only lets me draw one segment I think?)
Assign labels to all those segments
Save the segments and corresponding labels to be used further (not sure what format they would be stored in, I think imfreehand would give me the position and I could store that along with the labels?)
Hopefully use these labelled segments in the images to form a training dataset for a neural network.
If there is some other tool or software which would help me do this, then any pointers would be very much appreciated. (Also I am new to stackoverflow, so if there is any way I could improve on the question to make it clearer, please let me know!) Thanks!
Derek Hoiem, a computer vision research at the University of Illinois, wrote an object labelling tool which does pretty much exactly what you asked for. You can download it from his page:
http://www.cs.illinois.edu/homes/dhoiem/software/index.html
I'm trying to lay out images in a grid, with a few featured ones being 4x as big.
I'm sure it's a well known layout algorithm, but i don't know what it is called.
The effect I'm looking for is similar to the screenshot shown below. Can anyone point me in the right direction?
UPDATED
To be more specific, lets limit it to the case of there being only the two sizes shown in the example. There can be an infinite number of items, with a set margin between them. Hope that clarifies things.
There is a well-known layout algorithm called treemapping, which is perhaps a bit too generic for your specific problem with some images being 4x as big, but could still be applicable particularly if you decide you want to have arbitrary sizes.
There are several different rectangular treemap algorithms, any of which could be used to visualise photos. Here is a nice example, which uses the strip algorithm to lay out photos with each size proportional to the rating of the photo.
This problem can be solved with a heatmap or a treemap. Heatmaps often use space-filling-curves. A heatmap reduces the 2d complexity to a 1d complexity. A heatmap looks like a quadtree. You want to look for Nick's hilbert curve quadtree spatial index blog.
I'm trying to do top hat filtering in MATLAB. The imtophat function looks promising, but I have no idea how to use it. I don't have a lot of work with MATLAB before. I am trying to look find basically small spots several pixels wide that are local maxima in my 2 dimensional array.
I think you have more problem undertanding how to use STREL, than IMTOPHAT. The later can be described as simple threshold but per structural element, not the whole image.
Here is another good examples of using STREL and IMTOPHAT:
http://www.mathworks.com/matlabcentral/fx_files/2573/1/content/html/R14_MicroarrayImage_CaseStudy.html
This series of posts on Steve Eddins blog might be useful for you:
http://blogs.mathworks.com/steve/category/dilation-algorithms/
tophat is basically an "opening" procedure followed by a subtraction of the result from the original image. the best and most helpful explanation of opening I've found here:
http://homepages.inf.ed.ac.uk/rbf/HIPR2/morops.htm
"The effect of opening can be quite easily visualized. Imagine taking
the structuring element and sliding it around inside each foreground
region, without changing its orientation. All pixels which can be
covered by the structuring element with the structuring element being
entirely within the foreground region will be preserved. However, all
foreground pixels which cannot be reached by the structuring element
without parts of it moving out of the foreground region will be eroded
away."
The documentation on imtophat has an example .. did you try it? The following images are from the MATLAB documentation.
Code
I = imread('rice.png');
imshow(I)
se = strel('disk',12);
J = imtophat(I,se);
figure, imshow(J,[])
Original
(image source: mathworks.com)
Top Hat with a disk structuring element
(image source: mathworks.com)