析取范式模型。
DISJUNCTIVE NORMAL SHAPE MODELS.
作者信息
Ramesh Nisha, Mesadi Fitsum, Cetin Mujdat, Tasdizen Tolga
机构信息
Department of Electrical and Computer Engineering, University of Utah, United States ; Scientific Computing and Imaging Institute, University of Utah, United States.
Faculty of Engineering and Natural Sciences, Sabanci University, Turkey.
出版信息
Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:1535-1539. doi: 10.1109/ISBI.2015.7164170.
A novel implicit parametric shape model is proposed for segmentation and analysis of medical images. Functions representing the shape of an object can be approximated as a union of polytopes. Each polytope is obtained by the intersection of half-spaces. The shape function can be approximated as a disjunction of conjunctions, using the disjunctive normal form. The shape model is initialized using seed points defined by the user. We define a cost function based on the Chan-Vese energy functional. The model is differentiable, hence, gradient based optimization algorithms are used to find the model parameters.
提出了一种用于医学图像分割和分析的新型隐式参数形状模型。表示物体形状的函数可以近似为多个多面体的并集。每个多面体通过半空间的交集获得。使用析取范式，形状函数可以近似为合取的析取。形状模型使用用户定义的种子点进行初始化。我们基于Chan-Vese能量泛函定义了一个代价函数。该模型是可微的，因此，使用基于梯度的优化算法来寻找模型参数。
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