CRF-driven Implicit Deformable Model

G. Tsechpenakis, Dimitris N. Metaxas
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引用次数: 31

Abstract

We present a topology independent solution for segmenting objects with texture patterns of any scale, using an implicit deformable model driven by conditional random fields (CRFs). Our model integrates region and edge information as image driven terms, whereas the probabilistic shape and internal (smoothness) terms use representations similar to the level-set based methods. The evolution of the model is solved as a MAP estimation problem, where the target conditional probability is decomposed into the internal term and the image-driven term. For the later, we use discriminative CRFs in two scales, pixel- and patch-based, to obtain smooth probability fields based on the corresponding image features. The advantages and novelties of our approach are (i) the integration of CRFs with implicit deformable models in a tightly coupled scheme, (ii) the use of CRFs which avoids ambiguities in the probability fields, (iii) the handling of local feature variations by updating the model interior statistics and processing at different spatial scales, and (v) the independence from the topology. We demonstrate the performance of our method in a wide variety of images, from the zebra and cheetah examples to the left and right ventricles in cardiac images.
crf驱动的隐式变形模型
我们提出了一种拓扑无关的解决方案,用于分割具有任意尺度纹理图案的物体,使用由条件随机场(CRFs)驱动的隐式可变形模型。我们的模型将区域和边缘信息集成为图像驱动项,而概率形状和内部(平滑)项使用类似于基于水平集的方法的表示。将模型的演化分解为MAP估计问题,将目标条件概率分解为内部项和图像驱动项。对于后者,我们使用基于像素和基于补丁的两种尺度的判别crf,根据相应的图像特征获得光滑的概率场。该方法的优点和新颖之处在于:(i)在紧密耦合方案中将crf与隐式可变形模型集成,(ii)使用crf避免了概率域中的模糊性,(iii)通过更新模型内部统计和在不同空间尺度上进行处理来处理局部特征变化,以及(v)与拓扑的独立性。我们在各种各样的图像中展示了我们的方法的性能,从斑马和猎豹的例子到心脏图像中的左心室和右心室。
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