UM-BMID

The University of Manitoba Breast Microwave Imaging Dataset (UM-BMID) is an open-access dataset available to all researchers. The dataset contains data from experimental scans of MRI-derived breast phantoms.

The dataset itself can be found here. The shortened link is https://bit.ly/UM-bmid (case sensitive).

The dataset is described in this article, and this article should be cited in any work utilizing the dataset:

T. Reimer, J. Krenkevich, and S. Pistorius, "An open-access experimental dataset for breast microwave imaging,", in 2020 European Conference on Antennas and Propagation (EuCAP 2020), Copenhagen, Denmark, Mar. 2020, pp. 1-5, doi:10.23919/EuCAP48036.2020.9135659.

This GitHub repository contains the code used to produce the results presented in that paper and supportive scripts for the UM-BMID dataset.

Getting Started

Accessing and Downloading UM-BMID

The dataset itself (and accompanying documentation) can be found here (https://bit.ly/UM-bmid, case sensitive).

Rather than downloading the entire dataset, we recommend finding individual files that interest you, and only downloading those specific files (due to the size of the dataset). The file /docs/UM-BMID_README.txt (found here) describes the folder structure of the Google Drive and contains information about the individual data files.

Prerequisites

This repository contains both Python and Matlab/Octave files. The Python requirements are:

• Python 3.6 (or more recent)

• Libraries in the requirements.txt file

  • numpy >= 1.16.2
  • pathlib >= 1.0.1
  • scipy >= 1.2.1
  • matplotlib >= 3.0.3

The Matlab/Octave requirements are:

• Matlab 2017a (or more recent)

Installing

We recommend using the Anaconda distribution for Python 3.x, which can be downloaded here.

After installing a Python distribution, the required libraries can be installed via the command line. After navigating to the project directory, enter the command:

pip install -r requirements.txt

This will install all the libraries listed in the requirements.txt file.

Usage

Running the Tests

The UM-BMID/tests/ folder contains two Python test files and one Matlab/Octave test file.

• The /tests/check_requirements.py file checks if the required libraries are installed.

• The /tests/dataset_test.py and /tests/datsetTest.m files check if the dataset files from here have been placed in the /datasets/ folder in the project.

Exploring the Dataset

The best way to explore the dataset is to download the following files:

• UM-BMID/scan-data/gen-{one, two}/clean/fd_data_s11_emp.{mat, pickle}
• UM-BMID/scan-data/gen-{one, two}/clean/md_list_s11_emp.{mat, pickle}

The first file contains the measured frequency-domain S₁₁ parameters of all scans in that generation of the dataset, after having performed empty-chamber reference subtraction. The second file contains the metadata for each of these scans.

Alternatively, to use data without any reference-subtraction, download the files:

• UM-BMID/scan-data/gen-{one, two}/simple-clean/{python, matlab}-data/fd_data_gen_{one, two}_s11.{pickle, mat}
• UM-BMID/scan-data/gen-{one, two}/simple-clean/{python, matlab}-data/metadata_gen_{one, two}.{pickle, mat}

These files contain the frequency-domain S₁₁ parameters of all scans, including empty-chamber reference scans.

Data Use Examples

An example demonstrating usage of the simple-data files is described here. To begin, download the files:

• UM-BMID/scan-data/gen-two/simple-clean/python-data/fd_data_gen_two_s11.pickle
• UM-BMID/scan-data/gen-two/simple-clean/python-data/metadata_gen_two.pickle

and place them in your local UM-BMID repository under the folder:

• UM-BMID/scan-data/gen-two/simple-clean/python-data/

After downloading the files and placing them in the folder in your local UM-BMID project directory, use the ../run/simple_data_use_ex.py file to explore loading and using the dataset corresponding metadata.

Two sample files for using the clean dataset files are contained in the /run/ folder: the /run/dataUseEx.m and /run/data_use_ex.py files. These files demonstrate how to import the clean dataset files, display the sinogram measured from an experimental scan, and access the metadata for that experimental scan.

More information can be found in the README.md within the /run/ folder.

Contributing

Please read the CONTRIBUTING.md for details on contributing to the project.

Authors

• Tyson Reimer, University of Manitoba, Department of Physics & Astronomy, Winnipeg, Manitoba
• Jordan Krenkevich, University of Manitoba, Department of Physics & Astronomy, Winnipeg, Manitoba
• Dr. Stephen Pistorius, University of Manitoba, Department of Physics & Astronomy, Winnipeg, Manitoba

License

This project is licensed under the Apache 2.0 License. See the LICENSE file for more information.

Acknowledgments

The authors would like to thank Masoud Kamely and Hillary Kroeker for their assistance in performing some of the experimental scans for UM-BMID. The authors would also like to thank Jorge Sacristan for many valuable discussions.