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Biomedical Image Analysis

Analyse and treat biomedical images to explore computacional linear algebra concepts and learn how to use masks and filters to extract the necessary data.

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📃 Explanation

This project was created to study and analyse biomedical images with DICOM extensions, like this:

example

DICOM is an acronym for Digital Imaging and Communications in Medicine. Files in this format are most likely saved with either a DCM or DCM30 (DICOM 3.0) file extension.

The objective is to load the data images of a human chest, use necessary filters and understand how to see possible problems.

Details

More technical details can be observed in the following file:

biomedical_image_analysis.ipynb

Steps

  1. Introduction: Import the necessary libraries to read every DICOM medical image.
  2. Exploratory Data Analysis: Load, construct and navigate in N-dimensional images using CT scans of human chests.
  3. Usage of masks and filters in biomedical images: Use grayscales to facilitate the processing.
  4. Biomedical images measures and comparision
  5. Conclusion

🛠️ Technologies used

The following technologies were used:

  • Languages:
    • Python
  • Libraries: 
    # Main library to resolve the problem.
import imageio as iio
# Library to help in images visualization.
import matplotlib.pyplot as plt

📑 License

This project is under the MIT license. See the LICENSE file for more details.

Made with 🧡 by Jhonatan Oliveira.