Gist 4bbeb39fd0c727966cbbac7d8b41ebc1

Gist f8ef22f4884653740625923059e1acfb

This example processes brain scan sprite images for use in a web-based visualization. It loads PNG files from a local directory, replaces pure black pixels with white, and then applies histogram equalization and autocontrast using Pillow’s `ImageOps` and `ImageEnhance`. The cleaned images are saved to a new path, with the code iterating over a list of subject IDs read from a text file via `np.genfromtxt`. It reports completion or errors for each subject, demonstrating a batch image-preprocessing pipeline for preparing neuroimaging data.

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Gist 156e9d479d1f72e7656463843af8fd6d

This notebook demonstrates how to convert Mindboggle neuroimaging outputs into a format compatible with Mindcontrol, a web-based visualization tool. It shows the process of generating structured entries that link brain segmentation results, T1-weighted MRI images, and associated metrics for each subject. The code uses Python libraries including pandas for handling demographic data, nipype for JSON file management, and custom functions to extract file paths and create metadata entries. The visualization approach relies on organizing subject-specific data into JSON-ready dictionaries that Mindcontrol can consume for interactive display.

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Gist 441d9e9e69dfd1c303a0c0b76719a9cb

This example visualizes brain connectivity by computing a streamline connectivity matrix from diffusion tractography data. The notebook uses DIPY’s `utils.connectivity_matrix` to map streamlines to cortical and deep gray matter regions, then writes the resulting metric matrices to CSV files. It loads track-vis streamlines and FreeSurfer-labeled anatomical images with nibabel, applies a voxel-to-world affine transformation, and reduces labels to relevant gray matter regions. The code also demonstrates how to filter streamlines for specific functional regions using custom region maps, and relies on FSL and FreeSurfer tools for image processing.

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Gist dd6562218ed80aaca786

This example demonstrates a spectral clustering workflow for neuroimaging data, using scikit-learn’s `SpectralClustering` to segment thalamus voxels into distinct clusters based on correlation matrices. It processes fMRI time-series data via Nipype pipelines, nibabel for image I/O, and matplotlib with `%pylab inline` for visualization. The notebook shows how clustering results are saved as NIfTI files, with parameters like subject ID, region of interest, and cluster count varied across runs.

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Gist 5f27eb42f27c58d522f1210fd3659f06

This visualization displays brain imaging results for a single subject, comparing fractional anisotropy (APM), mean diffusivity (MD), and B0 maps before and after a procedure. It automatically locates the world coordinates of the maximum APM difference and generates six static PNG plots, each showing a statistical map overlaid on a pre-APM background. The code uses Nilearn’s `plot_stat_map` and `plot_anat` functions with a jet colormap and black background, alongside NumPy for coordinate transforms and Matplotlib for figure styling.

Gist 61f729d5fdcda29e57ebc8ec0b06652f

This example generates image tiles from 3D neuroimaging data, converting MRI volumes into 2D slice images along a specified axis. It loads NIfTI files with nibabel, reorients the data to IPL coordinates, and renders each slice as a grayscale PNG using matplotlib. The code also creates JSON manifests that record slice indices, mask information, and file paths, enabling tile-based visualization of brain scans.

Gist 59ae1a59826b5b7e09a35c922852cb2b

This example prepares and summarizes a dataset of brain MRI slice images. The notebook loads a metadata CSV into a pandas DataFrame and verifies that all referenced image files exist. It then groups the data by subject and slice direction, counting the number of slices for each binary label (0 or 1) with `groupby` and `apply`, before restructuring the result with `unstack`. The output is a cross-tabulated summary showing slice counts per subject, direction, and label.

Gist 237fa77ce0984eaedbfd

A Vue-based visualization of brain MRI segmentation data, this example loads anatomical and labeled brain volumes using nibabel and renders them with nipy's OrthoSlicer. The notebook applies a threshold to isolate specific cortical regions, remaps label values, and displays the result as a colored overlay on the anatomical scan using an HSV colormap, producing a single axial slice visualization of segmented brain structures.