In my last post I talked about using Blender to render MRI volumes. This had the benefit of creating fairly nice static images or movies. In an earlier post I also showed how to use XTK to render volumes in real time.¹ In this post I'll show a similar method of rendering a brain volume in real time using three.js, a Javascript library providing facilities for WebGL.

This process is relatively straightforward. Using these steps from my last post you can get a Wavefront OBJ file from an MRI volume. This object file will have a very large number of polygons, which may make loading and rendering the object in WebGL slower. In order to reduce the number of polygons, import the Object file into Blender and apply the decimate modifier on each hemisphere. Then export the hemispheres as an OBJ file. My resulting OBJ file is linked here.

Then we can basically just modify the object loader three.js example to load our brain!

I outside of the normal minified three.js library I have one main script that handles the interactive rotation and placement of the WebGL container and I've slightly modified the OBJLoader.js script to compute vertex normals (geom.computeVertexNormals();), making the brain appear smoother.

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Footnotes:

1. XTK can actually handle FreeSurfer meshes, so using three.js isn't strictly necessary.

In one of my posts I talked about using rendering brain volumes in-browser using XTK. The results, I’ll admit, weren’t spectacular. The volume rendering didn’t really give very defined edges. But now I’ll show a couple methods of rendering a brain using Blender. The first method is using volumetric data in Blender, and the second uses surfaces generated by FreeSurfer. I think it gives pretty cool results, check it out below.

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In a couple of my previous posts I talked about using clustering colors with k-means and counting clusters with EM. This kind of clustering is fairly straightforward, as you have some notion of distance between points to judge similarity. But what if you wanted to cluster text? How do you judge similarity there? (There are certain measures you could use, like the F-measure, which I’ll talk about in a later post.)

One way is to use Latent Dirichlet Allocation, which I first heard about while talking to a Statistics 133 GSI, and then later learned about while reading probabilistic models of cognition. Latent Dirichlet Allocation is a generative model that describes how text documents could be generated probabilistically from a mixture of topics, where each topic has a distribution over words. For each word in a document, a topic is sampled, from which a word is then sampled. This model gives us probabilities of documents, given topic distribution and words. But what’s more interesting here is learning about topics given the observed documents.

Here’s the plate notation view of LDA, which describes exactly how documents are generated:

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Recently I’ve been playing around with interactive visualizations, using tools like d3.js and GGobi. One of the things I like about interactive visualizations, as opposed to static graphics, is that with interactive visualizations you don’t have to make all the information available at once. You can present a broad overview of your data. And by having the user query specific data points, you can present more data as needed. Take this example of airport flight connectivity in the United States. If you had to display all the airport names and all the connections in one graph, it’d probably look pretty gross and would be very confusing to disentangle.

Similary, with MRI data, it’s usually hard to see the big picture at once. MRI data is usually just displayed in 2D slices. If you’re showing activations you may show a couple slices, perhaps one axial and one sagittal, so your audience can get an idea of where your clusters take place. If you wanted to show a whole brain, you could perhaps do an animated GIF, like so.

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