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A Proposed Method for Brain Medical Image Registration by Hierarchical Clustering Algorithm

机译:一种基于层次聚类算法的脑医学图像配准方法

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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.
机译:随着医学成像技术的爆炸性增长,以人体的结构和功能组织为中心的研究数量也大大增加。因此,使用神经科学数据面临专家两个主要问题。一个是大量的数据,另一个是数据的复杂性。许多科学家和医师正在不同方面进行大脑计划。捕捉和处理人脑图像并非易事。 Talairach大脑无法匹配单个扫描的事实促使我们使用其他类型的方法和算法。通过使用大脑解剖结构作为整合不同类型图像的来源,研究人员尝试在不同方面对人脑进行细分。通过利用分层聚类算法,我们尝试提出一种有效且更准确的人脑图像处理方法。

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