Tsechpenakis Gabriel, Lujan Brandon, Martinez Oscar, Gregori Giovanni, Rosenfeld Philip J
Dept. of Electrical and Computer Engineering, University of Miami, USA.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):883-91. doi: 10.1007/978-3-540-85988-8_105.
We present a geometric deformable model driven by dynamically updated probability fields. The shape is defined with the signed distance function, and the internal (smoothness) energy consists of a C1 continuity constraint, a shape prior, and a term that forces the zero-level of the shape distance function towards a connected form. The image probability fields are estimated by our collaborative Conditional Random Field (CoCRF), which is updated during the evolution in an active learning manner: it infers class posteriors in pixels or regions with feature ambiguities by assessing the joint appearance of neighboring sites and using the classification confidence. We apply our method to Optical Coherence Tomography fundus images for the segmentation of geographic atrophies in dry age-related macular degeneration of the human eye.
我们提出了一种由动态更新概率场驱动的几何可变形模型。形状由符号距离函数定义，内部（平滑）能量包括C1连续性约束、形状先验以及迫使形状距离函数的零水平趋向连通形式的一项。图像概率场由我们的协作条件随机场（CoCRF）估计，它在演化过程中以主动学习的方式进行更新：通过评估相邻位置的联合外观并使用分类置信度，推断具有特征模糊性的像素或区域中的类别后验概率。我们将我们的方法应用于光学相干断层扫描眼底图像，用于分割人眼干性年龄相关性黄斑变性中的地图样萎缩。
