NiiVue-NiiMath

niivue is a neuroimaging visualization tool that can be embedded into web pages or standalone applications. niimath is a clone of fslmaths that provides many image processing commands. This web page shows how niimath (after being compiled to WebAssembly) can be used as a NiiVue plugin to allow rapid image processing using familiar commands.

Live Demo

Try out this repository in a live demo

Compiling

You can host your own instance locally, allowing you to tume this distribution.

git clone https://github.com/niivue/niivue-niimath
cd niivue-niimath
npm install
npm run dev

Commands

You can use many of the commands familiar to fslmaths users:

• bandpass : Butterworth filter, highpass and lowpass in Hz,TR in seconds (zero-phase 2*2nd order filtfilt)
• bptfm : Same as bptf but does not remove mean (emulates fslmaths < 5.0.7)
• bwlabel : Connected component labelling for non-zero voxels (conn sets neighbors: 6, 18, 26)
• c2h : reverse h2c transform
• ceil : round voxels upwards to the nearest integer
• crop : remove volumes, starts with 0 not 1! Inputting -1 for a size will set it to the full range
• dehaze : set dark voxels to zero (mode 1..5; higher yields more surviving voxels)
• detrend : remove linear trend (and mean) from input
• demean : remove average signal across volumes (requires 4D input)
• edt : estimate Euler Distance Transform (distance field). Assumes isotropic input
• floor : round voxels downwards to the nearest integer
• mod : modulus fractional remainder - same as '-rem' but includes fractions
• otsu : binarize image using Otsu''s method (mode 1..5; higher yields more bright voxels))
• power : raise the current image by following exponent
• h2c : convert CT scans from 'Hounsfield' to 'Cormack' units to emphasize soft tissue contrast
• resize : grow (>1) or shrink (<1) image. Method (0=nearest,1=linear,2=spline,3=Lanczos,4=Mitchell)\n");
• round : round voxels to the nearest integer
• sobel : fast edge detection
• sobel_binary : sobel creating binary edge
• tensor_2lower : convert FSL style upper triangle image to NIfTI standard lower triangle order
• tensor_2upper : convert NIfTI standard lower triangle image to FSL style upper triangle order
• tensor_decomp_lower : as tensor_decomp except input stores lower diagonal (AFNI, ANTS, Camino convention)
• trunc : truncates the decimal value from floating point value and returns integer value
• unsharp : edge enhancing unsharp mask (sigma in mm, not voxels; 1.0 is typical for amount (scl))
• dog : difference of gaussian with zero-crossing edges (positive and negative sigma mm)

Voxels to Mesh

This project also includes the features of nii2mesh. While those features are described in more details on that page, here is a simple guide.

• Load the voxel-based volume you want to meshify. You can drag and drop an image or you can use the Volumes menu to load an image.
• Select one of the meshify items from the Operations menu (meshify medium is a good starting point).
• Press Process Image to apply your selected meshification.
• Optionally, if you want to further decimate your mesh, press the Simplify Mesh button. This will create smaller files, albeit with less detail.
• Press Save Mesh to save your mesh.

Note that you can run the fslmaths operations described in the previous section before your selected nii2mesh options. Consider the operation -s 2 -mesh -i m -l 1 -b 1 -s 100 -r 0.05. This will apply a 2mm smooth (-s 2) before creating a mesh. The mesh will use a medium intensity isosurface (-i m), only extract the largest object (-l 1), fill bubbles (-b 1) and smooth the mesh with 100 iterations (-s 100), reducing the mesh to only retain 5% of the original triangles (-r 0.05).

Referencing

This work is described in the following publication.

• Rorden C, Webster M, Drake C, Jenkinson M, Clayden JD, Li N, Hanayik T (2024) niimath and fslmaths: replication as a method to enhance popular neuroimaging tools. Aperture Neuro https://doi.org/10.52294/001c.94384