I'm currently working with DICOM-RT files (which contain DICOM along with dose delivery data and structure set files). I'm mainly interested in the "structure set" file (i.e. RTSS.dcm), which contains the set of contour points for an ROI of interest. In particular, the contour points surround a tumor volume. For instance, a tumor would have a set of 5 contours, each contour being a set of points that encircle that slice of the tumor.
I'm trying to use MatLab to use these contour points to construct a tumor volume in a binary 3D matrix (0 = nontumor, 1=tumor), and need help.
One possible approach is to fill each contour set as a binary slice, then interpolate the volume between slices. So far I've used the fill or patch function to create binary cross-sections of each contour slice, but I'm having difficulty figuring out how to interpolate these binary slices into a 3D volume. None of the built-in functions appear to apply to this particular problem (although maybe I'm just using them wrong?). A simple linear interpolation doesn't seem appropriate either, since the edges of one contour should blend into the adjacent contour in all directions.
Another option would be to take the points and tesselate them (without making slices first). However, I don't know how to make MatLab only tesselate the surface of the tumor and not intersecting the tumor volume. Currently it seems to find triangles within the tumor. If I could get it into just a surface, I'm not sure how to take that and convert it into a binary 3D matrix volume either.
Does anyone have experience with either 3D slice interpolation OR tesselation techniques that might apply here? Or perhaps any relevant toolkits that exist? I'm stuck... :(
I'm open to approaches in other languages as well: I'm somewhat familiar with C# and Python, although I assumed MatLab would handle the matrix operations a little easier.
Thanks in advance!
I'm not sure from what program you're exporting your dicom-rt structure files, but I believe I found a more elegant solution for you, already described in an open-source software (GDCM, CMake, ITK) in an Insight journal article.
I was discussing a similar problem with one of our physicists, and we saw your solution. It's fine if whatever structure you're attempting to binarize has no concavities, but if so, they'll be rendered inaccurately.
This method is verified for dicom-rt structure sets from Eclipse and Masterplan. Hope it helps.
http://www.midasjournal.org/download/viewpdf/701/4
I think I found an answer in another post (here). Rather than trying to interpolate the "missing slices" between the defined contours, treating the contour points as a point cloud and finding the convex hull might be a more efficient way of doing it. This method created the binary 3D volume that I was after.
Here is the code I used, hope it might be helpful to those who need to work with DICOM-RT files:
function mask = DicomRT2BinaryVol(file)
points = abs(getContourPoints(file));
%%NOTE: The getContourPoints function simply reads the file using
%%'dicominfo' method and organizes the contour points into an n-by-3
%%matrix, each column being the X,Y,Z coordinates.
DT = DelaunayTri(points);
[X,Y,Z] = meshgrid(1:50,1:50,1:50);
simplexIndex = pointLocation(DT, X(:), Y(:), Z(:));
mask = ~isnan(simplexIndex);
mask = reshape(mask,size(X));
end
This method is a slightly modified version of the method posted by #gnovice in the link above.
iTk is an excellent library for this sort of thing: http://www.itk.org/
HTH
Related
I have 8 plots which I want to implement in my Matlab code. These plots originate from several research papers, hence, I need to digitize them first in order to be able to use them.
An example of a plot is shown below:
This is basically a surface plot with three different variables. I know how to digitize a regular plot with just X and Y coordinates. However, how would one digitize a graph like this? I am quite unsure, hence, the question.
Also, If I would be able to obtain the data from this plot. How would you be able to utilize it in your code? Maybe with some interpolation and extrapolation between the given data points?
Any tips regarding this topic are welcome.
Thanks in advance
Here is what I would suggest:
Read the image in Matlab using imread.
Manually find the pixel position of the left bottom corner and the upper right corner
Using these pixels values and the real numerical value, it is simple to determine the x and y value of every pixel. I suggest you use meshgrid.
Knowing that the curves are in black, then remove every non-black pixel from the image, which leaves you only with the curves and the numbers.
Then use the function bwareaopen to remove the small objects (the numbers). Don't forget to invert the image to remove the black instead of the white.
Finally, by using point #3 and the result of point #6, you can manually extract the data of the graph. It won't be easy, but it will be feasible.
You will need the data for the three variables in order to create a plot in Matlab, which you can get either from the previous research or by estimating and interpolating values from the plot. Once you get the data though, there are two functions that you can use to make surface plots, surface and surf, surf is pretty much the same as surface but includes shading.
For interpolation and extrapolation it sounds like you might want to check out 2D interpolation, interp2. The interp2 function can also do extrapolation as well.
You should read the documentation for these functions and then post back with specific problems if you have any.
I am trying to detect corners (x/y coordinates) in 2D scatter vectors of data.
The data is from a laser rangefinder and our current platform uses Matlab (though standalone programs/libs are an option, but the Nav/Control code is on Matlab so it must have an interface).
Corner detection is part of a SLAM algorithm and the corners will serve as the landmarks.
I am also looking to achieve something close to 100Hz in terms of speed if possible (I know its Matlab, but my data set is pretty small.)
Sample Data:
[Blue is the raw data, red is what I need to detect. (This view is effectively top down.)]
[Actual vector data from above shots]
Thus far I've tried many different approaches, some more successful than others.
I've never formally studied machine vision of any kind.
My first approach was a homebrew least squares line fitter, that would split lines in half resurivly until they met some r^2 value and then try to merge ones with similar slope/intercepts. It would then calculate the intersections of these lines. It wasn't very good, but did work around 70% of the time with decent accuracy, though it had some bad issues with missing certain features completely.
My current approach uses the clusterdata function to segment my data based on mahalanobis distance, and then does basically the same thing (least squares line fitting / merging). It works ok, but I'm assuming there are better methods.
[Source Code to Current Method] [cnrs, dat, ~, ~] = CornerDetect(data, 4, 1) using the above data will produce the locations I am getting.
I do not need to write this from scratch, it just seemed like most of the higher-class methods are meant for 2D images or 3D point clouds, not 2D scatter data. I've read a lot about Hough transforms and all sorts of data clustering methods (k-Means etc). I also tried a few canned line detectors without much success. I tried to play around with Line Segment Detector but it needs a greyscale image as an input and I figured it would be prohibitivly slow to convert my vector into a full 2D image to feed it into something like LSD.
Any help is greatly appreciated!
I'd approach it as a problem of finding extrema of curvature that are stable at multiple scales - and the split-and-merge method you have tried with lines hints at that.
You could use harris corner detector for detecting corners.
I am working with structured 2.5D and unstructured 3D data, which generally is available in (X,Y,Z) coordinates, i.e. point clouds. Now I want to impose a regular voxel grid onto the data. This is not meant for visualization purposes, but rather for "cleaning" or fusing the data. I imagine cases, where e.g. 3 points fall within the volume of one voxel. Then I would aim at either just setting this voxel to "activated" and discarding the 3 original points or alternatively I would like to calculate the euclidian mean of the points and return the thus "cleaned" point cloud as an irregularly structured one again.
I hope I could make my intentions clear enough: It's not about visualization and not necessarily about using volumetric cubes instead of points. It's only about manipulating possibily irregular point clouds in a structured way.
I was thinking about kd-tree or octree based solutions in this context, but can anybody point me in the proper direction? Hinting at existing MATLAB implementations would be most appreciated.
If the data is irregularly spaced, what you want to use is something which both smooths and interpolates your data points. A very good method for doing so is Gaussian process regression. Here's an example for the same problem but in 2D.
I have 3D space. And I know for example N points in this space (x1,y1,z1), (x2,y2,z2)..., (xn,yn,zn). I want to interplolate points, that is different from this. How can I do this in Matlab?
interp3 may help you. Here is the documentation.
As always, there are questions left unanswered by your one line query.
If the data is of the form where there is a functional relationship z(x,y), (or y(x,z) or x(y,z)) then you might potentially be able to use one of the interpolation tools. Thus, suppose you have data that lies on a lattice in the (x,y) plane, thus some value of z at each point in that lattice. In this case, you can use interp2.
Alternatively, if the data is scattered, but there is some single valued functional relationship z(x,y) that you don't have, but it is some continuous function. Infinite first derivatives are a problem too here. In this case, assuming that you have data that at least fills some convex domain in the (x,y) plane, you can still interpolate a value of z. For this, use griddata, or TriScatteredInterp. Or you might use my own gridfit tool, as found on the file exchange.
Next, the way you describe the data, I'm not at all positive that you have something in one of the above forms. For example, if your data lies along some curved path in this 3D domain, and you wish to interpolate points along that curved arc can be done using my interparc tool, also found on the file exchange.
One last case that people often seem to have when they talk about interpolation of a spatial set like this, is just a general surface, that they wish to build a neatly interpolated, smooth surface. It might be something as simple as the surface of a sphere, or something wildly more complex. (These things are never simple.) For this, you might be able to use a convex hull to approximate something, if it is a closed convex surface. More complex surfaces might require a tool like CRUST, although I have no implementation of it I can offer to you. Google will help you there, if that is what you need.
The point of all this is, how you interpolate your data depends on what form the data is in, what it represents, and the shape of the relationship you will be interpolating.
Is there any subroutine, in MATLAB, that takes in a list of points, and return me a good mesh that I can use to show to my colleagues, such as this?
Actually, all I need is just a simple 2D mesh generator that takes in a series of X, Y coordinates (that defines the boundary of the area), and give me back a list of elements that can mesh that area well. I can do the rest by using MATLAB command to interpolate the Z value.
Edit : I am not interested to use MATLAB to produce the above looking plot. I am interested in using a MATLAB library to obtain a list of elements so that when I plot those element myself (not in MATLAB itself; but in my own C# program), I can obtain this meshed surface.
PS: I know there is this DistMesh, but I am looking for something simpler - something built-in direct in MATLAB perhaps. And no, meshgrid is not mesh generation.
It sounds like you want to create a finite element mesh, starting with a set of points defining a boundary of a region and then generating a triangular mesh that creates more points within that region. I don't think there's a "simple" solution for this problem.
The closest "built-in" solution would probably be the Partial Differential Equation Toolbox, specifically some of the Geometry Algorithms like INITMESH and REFINEMESH.
The link you gave to DistMesh appears to be another good solution. There are also a few submissions on the MathWorks File Exchange that you could take a look at:
MESH2D by Darren Engwirda
Finite Element Toolbox 2.1 by Rasmus Anthin
That picture looks exactly like the one from the griddata documentation. The example in there looks like what you want.
SFTOOL will easily make the picture that you show.
A thin-plate spline, e.g., TPAPS, should also do the job.
I think the user-created 'gridfit' is the best I've come across for a single surface, much better/prettier than griddata.
Mesh generation as in Delaunay Triangulation + Steiner Points? There is a builtin Delaunay function in MATLAB.
If your surface is the z=f(x,y) form you can use:
http://www.advancedmcode.org/how-to-plot-a-coloured-surface-from-3d-scatter.html
If your surface is concave look for surface reconstruction on the same website.