Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.image.index_img¶

nilearn.image.index_img(imgs, index)[source]¶

Indexes into a image in the last dimension.

Common use cases include extracting an image out of img or creating a 4D (or 2D for surface) image whose data is a subset of img data.

Parameters:

imgs4D Niimg-like object or 2D SurfaceImage: See Input and output: neuroimaging data representation.
indexAny type compatible with numpy array indexing: Used for indexing the data array in the last dimension.

Returns:

Nifti1Image or SurfaceImage: Indexed image.

See also

nilearn.image.concat_imgs
nilearn.image.iter_img

Examples

First we concatenate two MNI152 images to create a 4D-image:

>>> from nilearn import datasets
>>> from nilearn.image import concat_imgs, index_img
>>> joint_mni_image = concat_imgs([datasets.load_mni152_template(),
...                                datasets.load_mni152_template()])
>>> print(joint_mni_image.shape)
(197, 233, 189, 2)

We can now select one slice from the last dimension of this 4D-image:

>>> single_mni_image = index_img(joint_mni_image, 1)
>>> print(single_mni_image.shape)
(197, 233, 189)

We can also select multiple frames using the slice constructor:

>>> five_mni_images = concat_imgs([datasets.load_mni152_template()] * 5)
>>> print(five_mni_images.shape)
(197, 233, 189, 5)

>>> first_three_images = index_img(five_mni_images,
...                                slice(0, 3))
>>> print(first_three_images.shape)
(197, 233, 189, 3)

Examples using `nilearn.image.index_img`¶

3D and 4D niimgs: handling and visualizing

A introduction tutorial to fMRI decoding

Visualizing a probabilistic atlas: the default mode in the MSDL atlas

Cortical surface-based searchlight decoding

Decoding of a dataset after GLM fit for signal extraction

Decoding with ANOVA + SVM: face vs house in the Haxby dataset

Decoding with FREM: face vs house vs chair object recognition

Different classifiers in decoding the Haxby dataset

ROI-based decoding analysis in Haxby et al. dataset

Searchlight analysis of face vs house recognition

Setting a parameter by cross-validation

Understanding Decoder

Clustering methods to learn a brain parcellation from fMRI

Regions extraction using dictionary learning and functional connectomes

Regions Extraction of Default Mode Networks using Smith Atlas

Simple example of NiftiMasker use

Understanding NiftiMasker and mask computation

Advanced decoding using scikit learn

Massively univariate analysis of face vs house recognition

Multivariate decompositions: Independent component analysis of fMRI

nilearn.image.index_img¶

Examples using nilearn.image.index_img¶

Examples using `nilearn.image.index_img`¶