...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Expert Systems with Application >Deformation based feature selection for Computer Aided Diagnosis of Alzheimer's Disease
【24h】

Deformation based feature selection for Computer Aided Diagnosis of Alzheimer's Disease

机译：基于变形的特征选择用于阿尔茨海默氏病的计算机辅助诊断

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

Deformation-based Morphometry (DBM) allows detection of significant morphological differences of brain anatomy, such as those related to brain atrophy in Alzheimer's Disease (AD). DBM process is as follows: First, performs the non-linear registration of a subject's structural MRi volume to a reference template. Second, computes scalar measures of the registration's deformation field. Third, performs across volume statistical group analysis of these scalar measures to detect effects. In this paper we use the scalar deformation measures for Computer Aided Diagnosis (CAD) systems for AD. Specifically this paper deals with feature extraction methods over five such scalar measures. We evaluate three supervised feature selection methods based on voxel site significance measures given by Pearson correlation, Bhattacharyya distance and Welch's t-test, respectively. The CAD system discriminating between healthy control subjects (HC) and AD patients consists of a Support Vector Machine (SVM) classifier trained on the DBM selected features. The paper reports experimental results on structural MRI data from the cross-sectional OASIS database. Average 10-fold cross-validation classification results are comparable or improve the state-of-the-art results of other approaches performing CAD from structural MRI data. Localization in the brain of the most discriminant deformation voxel sites is in agreement with findings reported in the literature.
机译：基于变形的形态计量学（DBM）可以检测大脑解剖结构的重大形态差异，例如与阿尔茨海默病（AD）中的脑萎缩相关的形态差异。 DBM过程如下：首先，将对象的结构MRi体积非线性注册到参考模板。其次，计算套准变形场的标量度量。第三，对这些标量度量进行跨数量统计组分析，以检测效果。在本文中，我们将标量变形量度用于AD的计算机辅助诊断（CAD）系统。具体来说，本文讨论了五个此类标量测度的特征提取方法。我们分别基于Pearson相关性，Bhattacharyya距离和Welch's t检验给出的体素位点显着性度量，评估了三种监督特征选择方法。区分健康对照对象（HC）和AD患者的CAD系统由在DBM所选功能上训练的支持向量机（SVM）分类器组成。该论文报告了来自横截面OASIS数据库的结构MRI数据的实验结果。平均10倍交叉验证分类结果可比或改善了其他从结构MRI数据执行CAD的方法的最新结果。最判别变形体素在大脑中的定位与文献报道的发现相符。

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