...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Spatial and Anatomical Regularization of SVM: A General Framework for Neuroimaging Data
【24h】

Spatial and Anatomical Regularization of SVM: A General Framework for Neuroimaging Data

机译：SVM的空间和解剖学正则化：神经影像数据的通用框架

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents a framework to introduce spatial and anatomical priors in SVM for brain image analysis based on regularization operators. A notion of proximity based on prior anatomical knowledge between the image points is defined by a graph (e.g., brain connectivity graph) or a metric (e.g., Fisher metric on statistical manifolds). A regularization operator is then defined from the graph Laplacian, in the discrete case, or from the Laplace-Beltrami operator, in the continuous case. The regularization operator is then introduced into the SVM, which exponentially penalizes high-frequency components with respect to the graph or to the metric and thus constrains the classification function to be smooth with respect to the prior. It yields a new SVM optimization problem whose kernel is a heat kernel on graphs or on manifolds. We then present different types of priors and provide efficient computations of the Gram matrix. The proposed framework is finally applied to the classification of brain Magnetic Resonance (MR) images (based on Gray Matter (GM) concentration maps and cortical thickness measures) from 137 patients with Alzheimer's Disease (AD) and 162 elderly controls. The results demonstrate that the proposed classifier generates less-noisy and consequently more interpretable feature maps with high classification performances.
机译：本文提出了一个框架，在基于正则化算子的SVM中为脑图像分析引入空间和解剖先验。基于图像点之间的先前解剖学知识的接近度的概念由图（例如，大脑连通性图）或度量（例如，统计流形上的费希尔度量）来定义。然后，在离散情况下从图拉普拉斯算子或在连续情况下从Laplace-Beltrami算子定义正则化算子。然后将正则化运算符引入到SVM中，它相对于图形或度量按指数级地惩罚高频分量，从而使分类函数相对于先验约束平滑。这就产生了一个新的SVM优化问题，其核心是图形或流形上的热核。然后，我们介绍不同类型的先验，并提供Gram矩阵的有效计算。所提出的框架最终应用于137名阿尔茨海默氏病（AD）患者和162名老年对照的脑磁共振（MR）图像分类（基于灰度（GM）浓度图和皮质厚度测量值）。结果表明，提出的分类器产生的噪声较小，因此具有较高的分类性能，可解释性更高。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号