联合图像与形状分析框架
A Framework for Joint Image-and-Shape Analysis.
作者信息
Gao Yi, Tannenbaum Allen, Bouix Sylvain
机构信息
Department of Electrical and Computer Engineering and the Comprehensive Cancer Center, the University of Alabama at Birmingham; 1150 10th Avenue South, Birmingham, AL 35294.
Departments of Computer Science and Applied Mathematics/Statistics, Stony Brook University, Stony Brook, New York, 11794.
出版信息
Proc SPIE Int Soc Opt Eng. 2014 Mar 21;9034:90340V. doi: 10.1117/12.2043276.
Techniques in medical image analysis are many times used for the comparison or regression on the intensities of images. In general, the domain of the image is a given Cartesian grids. Shape analysis, on the other hand, studies the similarities and differences among spatial objects of arbitrary geometry and topology. Usually, there is no function defined on the domain of shapes. Recently, there has been a growing needs for defining and analyzing functions defined on the shape space, and a coupled analysis on both the shapes and the functions defined on them. Following this direction, in this work we present a coupled analysis for both images and shapes. As a result, the statistically significant discrepancies in both the image intensities as well as on the underlying shapes are detected. The method is applied on both brain images for the schizophrenia and heart images for atrial fibrillation patients.
医学图像分析技术多次用于图像强度的比较或回归。一般来说，图像的域是给定的笛卡尔网格。另一方面，形状分析研究任意几何和拓扑的空间对象之间的异同。通常，在形状域上没有定义函数。最近，对定义和分析形状空间上的函数以及对形状和定义在其上的函数进行耦合分析的需求日益增长。遵循这一方向，在这项工作中，我们提出了一种针对图像和形状的耦合分析方法。结果，检测到图像强度以及基础形状上具有统计学意义的差异。该方法应用于精神分裂症患者的脑部图像和心房颤动患者的心脏图像。
Zotero 插件