Discrete Models for High-Level Image Analysis

9803365Valen E. Johnson这个项目开发了自动图像分析的统计模型。 开发的图像模型是通过在模板图像中任意放置大量的点（称为小平面）来定义的。 分面点被分层组织，并且被布置在分辨率增加的网格上或者使用更复杂的标准来确定，例如，它们可以位于图像的尺度空间中的局部极值处。 对象形状是通过在树状结构中连接小平面来建立的，然后使用图像模板中的树的形状来对来自类的观察图像中的小平面树的预期形状进行建模。 典型地，三万到四万个刻面被定位在二维图像模板内，而四分之一到五十万个刻面被放置在三维图像集内（例如，磁共振图像或正电子发射断层摄影图像）。在该提议下生成的模型促进了对通用图像的计算高效的高级分析。 这些模型适用于广泛的图像模态，并且仅要求模板图像或原型图像可用于所研究的图像组。 给定该类的图像模板，可以在很少或没有操作员干预的情况下应用导出的模型，以获得可用图像数据的自动分割、对象识别和体积分析。

9803365Valen E. JohnsonThis project develops statistical models for automatic image analysis. The image models developed are defined by arbitrarily placing a large number of points, called facets, in a template image. The facet points are organized hierarchically, and are arranged either on grids of increasing resolution or determined using more sophisticated criteria, e.g., they may be located at local extrema in the scale-space of the image. Object shapes are established by connecting facets in a tree-like structure, and then using the shape of the tree in the image template to model expected shapes of facet trees in observed images from the class. Typically, thirty to forty thousand facets are positioned within a two-dimensional image template, whereas one-quarter to one-half million facets are placed within three-dimensional image sets (e.g., magnetic resonance images or positron emission tomography images).Models generated under this proposal facilitate computationally efficient high-level analysis of generic images. These models are applicable to a wide range of image modalities, and require only that a template image or prototype image be available for the group of images under investigation. Given an image template for the class, derived models can be applied with little or no operator intervention to obtain automatic segmentation, object identification, and volumetric analysis for available image data.