Joshi Shantanu H, Bowman Ian, Toga Arthur W, Van Horn John D
Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, 90095, USA.
Proc IEEE Int Symp Biomed Imaging. 2011 Mar 30:1117-1120. doi: 10.1109/ISBI.2011.5872597.
We introduce a new representation of cortical regions via distribution functions of their features. The distribution functions are estimated non-parametrically from the data and are observed to be non Gaussian. Cortical pattern matching is enabled by using the information-based Jensen-Shannon divergence as a measure between features. Our approach explicitly avoids pairwise registrations between brains, but instead focuses on modeling and discriminating between the cortical structural patterns. We demonstrate our approach on 120 subject brains from an Alzheimer's dataset, and present applications to clustering, classification, and dimension reduction.
我们通过皮质区域特征的分布函数引入了一种新的皮质区域表示方法。分布函数是从数据中进行非参数估计得到的，并且观察到它们是非高斯分布的。通过使用基于信息的 Jensen-Shannon 散度作为特征之间的度量来实现皮质模式匹配。我们的方法明确避免了大脑之间的成对配准，而是专注于对皮质结构模式进行建模和区分。我们在来自阿尔茨海默病数据集的 120 个受试者大脑上展示了我们的方法，并展示了其在聚类、分类和降维方面的应用。
