Brain Tissue Segmentation in MRI Images Using Random Forest Classifier and Gossip Based Neighborhood


Considering the importance of careful segmentation of medical images for deleanition of unhelthy tissues or locating and tracing of the tumor growth, it has been the subject of interest in many medical researches. In this paper we propose a gossip-based region growing algorithm to extract more accurate spatial features. The features will then be imported to a random forest. The random forest classifier is an ensemble classifier derived from the decision tree idea but with accuracy rates comparable to most of currently used classifiers. Although being a very strong classifier, random forest has rarely been studied in the field of medical segmentation. The brain scans segmentation with random forest showed promising results using the gossip-based region growing algorithm.

  • Abstract
  • Key Words
  • 1 Introduction
  • 2. Random Forest
  • 3. Gossip Algorithm
  • 4. Proposed Algorithm
  • 5. Experimaental Results
  • 6. Conclusion and Future Work
  • References

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