{ "info": { "author": "SIPBA@UGR", "author_email": "sipba@ugr.es", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering" ], "description": "mapBrain (Spherical Brain Mapping)\n===================\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1042388.svg)](https://doi.org/10.5281/zenodo.1042388)\n[![Documentation Status](//readthedocs.org/projects/mapbrain/badge/?version=latest)](https://mapbrain.readthedocs.io/en/latest/?badge=latest)\n\n\nA library to perform **Spherical Brain Mapping** on a 3D Brain Image. \n\nThe **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. \n\nSBM 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.\n\nA 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`.\n\n\nInstallation\n----------------\n`mapBrain` is now available via `pypi` and can be installed directly from:\n\n```python\npip install mapBrain\n```\n\nOtherwise, copy the *.py files directly to the working directory, and import the library with `import mapBrain`. \n\nUsage\n-----------------\nThe Statistical Brain Mapping is structured as a class that can be invoked from every script. The simplest approach would be using: \n```python\nimport mapBrain\nimport nibabel as nib\n\nimg = nib.load('MRIimage.nii')\nsbm = mapBrain.SphericalBrainMapping()\nmap = sbm.doSBM(img.get_data(), measure='average', show=True)\n```\nTo-Do\n-----------------\n- Add support for functions as objects\n- Add support for different sampling methods\n\nReferences\n---------------------\n1. 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)\n2. 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. \n3. 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. \n4. F.J. Mart\u00ednez-Murcia et al. *A Volumetric Radial LBP Projection of MRI Brain Images for the Diagnosis of Alzheimer\u2019s Disease*. **Lecture Notes in Computer Science** 9107, 19-28. 2015.\n5. 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.", "description_content_type": "", "docs_url": null, "download_url": "https://github.com/SiPBA/mapBrain/archive/0.9.1.tar.gz", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/SiPBA/mapBrain", "keywords": "brain,image,analysis,feature,neuroimaging,texture,mapping,visualization", "license": "GPL-3.0+", "maintainer": "", "maintainer_email": "", "name": "mapBrain", "package_url": "https://pypi.org/project/mapBrain/", "platform": "", "project_url": "https://pypi.org/project/mapBrain/", "project_urls": { "Download": "https://github.com/SiPBA/mapBrain/archive/0.9.1.tar.gz", "Homepage": "https://github.com/SiPBA/mapBrain" }, "release_url": "https://pypi.org/project/mapBrain/0.9.3/", "requires_dist": null, "requires_python": "", "summary": "Brain image feature extraction and visualization", "version": "0.9.3" }, "last_serial": 4057365, "releases": { "0.9.3": [ { "comment_text": "", "digests": { "md5": "b37abdaeffc81535ba1059b7fe0624f7", "sha256": "38a04752974fbba188077cb772d421bc20543c8e6f50a51ea1049dcb9c59370e" }, "downloads": -1, "filename": "mapBrain-0.9.3.tar.gz", "has_sig": false, "md5_digest": "b37abdaeffc81535ba1059b7fe0624f7", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 7653, "upload_time": "2018-07-13T10:23:47", "url": "https://files.pythonhosted.org/packages/0a/f1/9f3d19b67135b0b931e2a9bd9e1f6487a5f917aa43aee0c2872bd487ef03/mapBrain-0.9.3.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "b37abdaeffc81535ba1059b7fe0624f7", "sha256": "38a04752974fbba188077cb772d421bc20543c8e6f50a51ea1049dcb9c59370e" }, "downloads": -1, "filename": "mapBrain-0.9.3.tar.gz", "has_sig": false, "md5_digest": "b37abdaeffc81535ba1059b7fe0624f7", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 7653, "upload_time": "2018-07-13T10:23:47", "url": "https://files.pythonhosted.org/packages/0a/f1/9f3d19b67135b0b931e2a9bd9e1f6487a5f917aa43aee0c2872bd487ef03/mapBrain-0.9.3.tar.gz" } ] }