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Performs a Spherical Brain Mapping of a 3D Brain Image
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mapBrain (Spherical Brain Mapping)

DOI Documentation Status

A library to perform Spherical Brain Mapping on a 3D Brain Image.

The Spherical Brain Mapping (SBM) is a framework intended to map the internal structures and features of the brain onto a 2D image that summarizes all this information, as described in [1] and previously presented in [2] and [3]. 3D brain imaging, such as MRI or PET produces a huge amount of data that is currently analysed using uni or multivariate approaches.

SBM provides a new framework that allows the mapping of a 3D brain image to a two-dimensional space by means of some statistical measures. The system is based on a conversion from 3D spherical to 2D rectangular coordinates. For each spherical coordinate pair (theta,phi), a vector containing all voxels in the radius is selected, and a number of values are computed, including statistical values (average, entropy, kurtosis) and morphological values (tissue thickness, distance to the central point, number of non-zero blocks). These values conform a two-dimensional image that can be computationally or even visually analysed.

A new structural parametrization of MRI images has been added, using a modified hidden markov model to trace routes that follow minimal intensity change paths inside the brain, instead of the rectilinear paths used in typical SBM [4]. This file, currently only working in MATLAB, is contained in the file hmmPaths.m.


mapBrain is now available via pypi and can be installed directly from:

pip install mapBrain

Otherwise, copy the *.py files directly to the working directory, and import the library with import mapBrain.


The Statistical Brain Mapping is structured as a class that can be invoked from every script. The simplest approach would be using:

import mapBrain
import nibabel as nib

img = nib.load('MRIimage.nii')
sbm = mapBrain.SphericalBrainMapping()
map = sbm.doSBM(img.get_data(), measure='average', show=True)


  • Add support for functions as objects
  • Add support for different sampling methods


  1. F.J. Martinez-Murcia et al. Assessing Mild Cognitive Impairment Progression using a Spherical Brain Mapping of Magnetic Resonance Imaging. Journal of Alzheimer's Disease (Pre-print). 2018. DOI: 10.3233/JAD-170403
  2. F.J. Martinez-Murcia et al. A Spherical Brain Mapping of MR images for the detection of Alzheimer's Disease. Current Alzheimer Research 13(5):575-88. 2016.
  3. F.J. Martinez-Murcia et al. Projecting MRI Brain images for the detection of Alzheimer's Disease. Stud Health Technol Inform 207, 225-33. 2014.
  4. F.J. Martínez-Murcia et al. A Volumetric Radial LBP Projection of MRI Brain Images for the Diagnosis of Alzheimer’s Disease. Lecture Notes in Computer Science 9107, 19-28. 2015.
  5. F.J. Martinez-Murcia et al. A Structural Parametrization of the Brain Using Hidden Markov Models-Based Paths in Alzheimer's Disease. International Journal of Neural Systems 26(6) 1650024. 2016.
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