Skip to content

Tissue classification of T1-weighted brain MR acquisitions using a hidden markov random field model.

Notifications You must be signed in to change notification settings

derekwayne/brain_segmentation

Repository files navigation

Image Segmentation

Purpose

While considering the applications of two dimensional image segmentation I thought it would be interesting to apply the procedures to medical images such as an MRI scan. These results are a part of the pipeline for tumor extraction and can be used for surgical preparations. Segmentation is equivalently a classification problem, where the labels represent tissues present in the scan. Specifically I chose to work with three dimensional brain MR images. This is a very interesting extension of techniques used in flat images. The goal is to explore some of the modern literature on the topic and attempt to use neruoimaging packages in R to achieve an accurate segmentation.

Tools and Packages

  1. R: For statistical computing
  2. mritc: Various methods for MRI tissue classification.
  3. imager: Image processing library.
  4. ggplot2: Data visualizations.
  5. dplyr: Data manipulation.
  6. gridExtra: Functions for graphics with grid layouts.

Author

Acknowledgments and References

The methods used within this project are thanks to the work done by Yongyue Zhang et al. in the paper Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorith.

About

Tissue classification of T1-weighted brain MR acquisitions using a hidden markov random field model.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages