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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

[Multi-spectral MR image segmentation of brain].

Fugen Zhou1, Wenyan Liu, Xiaokuan Zhou

  • 1Image Processing Center, Beihang University, Beijing 100083, China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|April 26, 2008
PubMed
Summary

This study introduces a new multi-spectral MRI segmentation method using data fusion. The technique accurately distinguishes white matter, gray matter, and cerebrospinal fluid (CSF) compared to single-image methods.

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Area of Science:

  • Medical imaging analysis
  • Computational neuroscience
  • Image processing

Context:

  • Accurate segmentation of brain tissues in Magnetic Resonance (MR) images is crucial for neurological research and clinical diagnosis.
  • Existing single-modality MR image segmentation methods often face limitations in distinguishing between different tissue types, such as white matter, gray matter, and cerebrospinal fluid (CSF).

Purpose:

  • To develop and evaluate a novel multi-spectral MR image segmentation method that leverages multi-modality image fusion to improve accuracy.
  • To compare the performance of the proposed fusion-based method against traditional single-image segmentation techniques.

Summary:

  • A new method for segmenting multi-spectral MR images is presented, utilizing Fuzzy C-Means (FCM) clustering on individual images followed by data fusion.
  • This approach integrates information from multiple image modalities to achieve a more robust and accurate final segmentation.
  • Experimental results demonstrate superior performance in differentiating white matter, gray matter, and CSF.

Impact:

  • The proposed method offers enhanced accuracy in brain tissue segmentation, potentially leading to more precise quantitative analysis in neuroimaging studies.
  • This advancement could improve the reliability of automated diagnostic tools and facilitate a deeper understanding of brain structure and function.
  • The multi-modality fusion approach provides a valuable framework for improving MR image segmentation in various research and clinical applications.