ofgulban

"""Maximum intensity projection over one dimension for a window."""

import os
import numpy as np
from nibabel import load, Nifti1Image, save

nii = load('/path/to/file.nii.gz')

w = 10  # window, total width is double of this number

ofgulban

# Compile an MP4 from a folder containing PNGs
ffmpeg -y -i /path/to/movie_frame-%03d.png -vb 20M -c:v libx264 -vf fps=24 -pix_fmt yuv420p path/to/movie.mp4

# Convert mp4 into a high quality gif
ffmpeg -y -i path/to/movie.mp4 -vf "fps=24, scale=768:-1:flags=lanczos,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse" -loop 0 path/to/movie.gif

ofgulban

"""Polish manually edited MRI white & gray matter segmentations."""

import os
import nibabel as nb
import numpy as np
from scipy.ndimage import morphology, generate_binary_structure
from scipy.ndimage import gaussian_filter

# Segmentation file
FILE = '/path/to/segmentation_01.nii.gz'

ofgulban

"""Simulate the spurious correlation of ratios.

Run this script multiple times and see the correlations between variables.

Reference
---------
- Pearson, K. (1896). Mathematical Contributions to the Theory of Evolution.
On a Form of Spurious Correlation Which May Arise When Indices Are Used in the
Measurement of Organs. Proceedings of the Royal Society of London, 60, 489–498.
<https://doi.org/10.1098/rspl.1896.0076>

ofgulban

"""Used for rendering frames as png files."""

import sys
import os
import numpy as np
import pyvista as pv
import nibabel as nb

FILENAME = "/path/to/image3D.nii.gz"
OUTDIR = "/path/to/output"

ofgulban

% Convert BrainVoyager reions of interests in volume (VOI) to .vmr files
%
% Similar to draw VOI in VMR option in BV but assigns unique numbers for
% each voi.

clear all

vmr_file = '/path/to/file.vmr';
voi_file = '/path/to/file.voi';

ofgulban

"Make affine matrix of a nifti file identity matrix."

import os
import nibabel as nb
import numpy as np

NII = "/path/to/tile.nii.gz"

nii = nb.load(NII)
data = np.asarray(nii.dataobj)

ofgulban

"""Compute volume of non-zero voxels in a nifti image."""

import numpy as np
import nibabel as nb

INPUT = "/path/to/image.nii.gz"

# Load data
nii = nb.load(INPUT)
img = nii.get_fdata()

ofgulban

"""Masked-ness score by source and derivative zero count ratio for images."""

import numpy as np
import nibabel as nb

INPUT = "/path/to/image.nii.gz"

# =============================================================================

def maskedness_score(img):

ofgulban

% BrainVoyager SRF triangular mesh format to Wavefront OBJ format conversion.
%{
Dependencies
------------
1- Neuroelf should be added to Matlab path. Source: http://neuroelf.net/,
tested version: 1.0
2- Resulting file can be loaded to e.g. Meshlab.
%}

clear all;