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Evaluation of two segmentation methods on MRI brain tissue structures.

X Cai1, Y Hou, C Li

  • 1Dept. of Electr. & Comput. Eng. & Comput. Sci., Cincinnati Univ., OH 45221, USA. caixg@ececs.uc.edu

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
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This study compares narrow band level set and maximum a posteriori (MAP) methods for segmenting 15 brain structures using MRI data. The MAP method showed promising results, achieving over 70% Dice Similarity Index for key structures.

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Anatomy

Background:

  • Accurate segmentation of brain tissue structures from MRI is crucial for neurological research and clinical diagnosis.
  • Existing segmentation methods vary in accuracy and efficiency, necessitating comparative evaluations.

Purpose of the Study:

  • To evaluate and compare the performance of two distinct brain MRI segmentation methods: narrow band level set and maximum a posteriori (MAP) probability framework.
  • To assess the accuracy of these methods across 15 different brain tissue structures using Dice Similarity Index (DSI).

Main Methods:

  • Two segmentation algorithms were applied: narrow band level set and MAP-based pattern classification.
  • Experiments were conducted on 18 verified MRI brain datasets.

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  • Segmentation accuracy was quantified using the Dice Similarity Index (DSI) against radiologist-defined gold standards.

    • Main Results:

    • Both methods were evaluated on 15 brain tissue structures.
    • The MAP method achieved DSI scores over 70% for the left and right lateral ventricles.
    • Other structures like the third ventricle, caudate nucleus, globus pallidus, putamen, and thalamus achieved DSI scores above 60% with the MAP method.

    Conclusions:

    • The MAP-based pattern classification method demonstrates robust performance for segmenting various brain structures in MRI.
    • The narrow band level set method also shows potential, with specific structures yielding high accuracy.
    • Further research into the applicability and optimization of these methods is warranted for clinical and research settings.