Metadata-Version: 2.1
Name: fmri-anonymizer
Version: 0.2.11
Summary: Anonymize your DICOM and NIFTI files with this tool easily.
Home-page: https://caoslab.psy.cmu.edu:32443/hugoanda/fmri_anonymizer
Author: Hugo Angulo
Author-email: hugoanda@andrew.cmu.edu
License: MIT
Description: # fMRI Anonymizer
        
        This project contains a series of scripts that perform 2 essential steps:
        
        1. De-Identification
        2. De-Facing
        
        The main purpose of this project is to have an "all-purpose" application that can automatize the whole anonymization process on MRI, fMRI data. This includes DICOM, and NIFTI formats. 
        
        This application follows a "best-effort" approach in order to comply with [HIPAA regulations](https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html).
        
        ## Pre-requisites
        
        Since this is a module that leverages on other packages, there are 2 main dependencies that must be installed within your system prior to install this solution.
        
        You will need:
        1. **FSL**: Please follow [this link](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation/MacOsX) to install it.
           
           1.1. ***Make sure to install XQuartz before installing FSL. This is mentioned within their site.***
        2. **dcm2niix**: This is a dependency used to convert DICOM into NIFTI format.
        
            2.1. In order to install it, you can safely use ***[brew](https://formulae.brew.sh/formula/dcm2niix)***.
           
            2.2. You can also follow [this tutorial](https://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage).
        
        ## How to install it?
        
        In order to use this package, you will need to use the following command (recommended using it in a separate environment - like conda or venv):
        
        `pip install fmri-anonymizer`
        
        ## How to use it?
        
        This is pretty simple, you have 2 ways to use it:
        
        `python -m fmri_anonymizer -i <INPUT_FOLDER> --dicom --anonymize --deface YES -o <OUTPUT_FOLDER>`
        
        Or:
        
        `fmri_anonymizer -i <INPUT_FOLDER> --dicom --anonymize --deface YES -o <OUTPUT_FOLDER>`
        
        Here, a complete example:
        ```shell
        python -m fmri_anonymizer -i "<path_source_dicom_files>" --dicom True --nifti True --deface True --anonymize True -o "<output_path>"
        ```
        
        ## How to get some help?
        
        Simply type:
        
        `python -m fmri_anonymizer -h`
        
        
        
        H4ppy H4ck1n6!
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.7
Description-Content-Type: text/markdown
