一种基于层次聚类的脑医学图像配准方法

Hamidreza Pooshfam, R. Abdullah
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引用次数: 1

摘要

医学成像技术的爆炸式增长与以人体结构和功能组织为中心的研究数量的巨大增长相匹配。因此,处理神经科学数据的专家面临两个主要问题;一个是数据量大,另一个是数据的复杂性。许多科学家和医生都在不同方面致力于大脑项目。捕捉和处理人脑图像并不是一件容易的事。Talairach大脑无法匹配个人扫描的事实促使我们使用其他类型的方法和算法。研究人员利用大脑解剖学作为整合不同类型图像的来源,试图从不同方面分割人类大脑。本文试图利用层次聚类算法为人脑图像处理提供一种有效、准确的方法。
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A Proposed Method for Brain Medical Image Registration by Hierarchical Clustering Algorithm
The explosive growth in medical imaging technologies has been matched by a tremendous increase in the number of investigations centred on the structural and functional organisation of the human body. Therefore working with neuroscientific data has faced experts with two major problems; one is the large amount of data and the other is complexity of it. Many scientists and physicians are working on Brain Projects in different aspects. Capturing and processing human brain images are not easy tasks. The fact that the Talairach brain fails to match individual scans motivate us to use other type of approaches and algorithms. With using brain anatomy as a source for integrating different types of images, researchers try to segment the human brain in different aspects. By taking advantage of hierarchical clustering algorithm we try to present an effective and more accurate approach for human brain image processing.
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