Metadata-Version: 1.1
Name: mapBrain
Version: 0.9.3
Summary: Brain image feature extraction and visualization
Home-page: https://github.com/SiPBA/mapBrain
Author: SIPBA@UGR
Author-email: sipba@ugr.es
License: GPL-3.0+
Download-URL: https://github.com/SiPBA/mapBrain/archive/0.9.1.tar.gz
Description: mapBrain (Spherical Brain Mapping)
        ===================
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1042388.svg)](https://doi.org/10.5281/zenodo.1042388)
        [![Documentation Status](//readthedocs.org/projects/mapbrain/badge/?version=latest)](https://mapbrain.readthedocs.io/en/latest/?badge=latest)
        
        
        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`.
        
        
        Installation
        ----------------
        `mapBrain` is now available via `pypi` and can be installed directly from:
        
        ```python
        pip install mapBrain
        ```
        
        Otherwise, copy the *.py files directly to the working directory, and import the library with `import mapBrain`. 
        
        Usage
        -----------------
        The Statistical Brain Mapping is structured as a class that can be invoked from every script. The simplest approach would be using: 
        ```python
        import mapBrain
        import nibabel as nib
        
        img = nib.load('MRIimage.nii')
        sbm = mapBrain.SphericalBrainMapping()
        map = sbm.doSBM(img.get_data(), measure='average', show=True)
        ```
        To-Do
        -----------------
        - Add support for functions as objects
        - Add support for different sampling methods
        
        References
        ---------------------
        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](https://zenodo.org/record/1162669)
        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. 
        
Keywords: brain,image,analysis,feature,neuroimaging,texture,mapping,visualization
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
