Similarity-based appearance-prior for fitting a subdivision mesh in gene expression images.
Similarity-based appearance-prior for fitting a subdivision mesh in gene expression images.
复制标题
基于相似性的外观先验，用于在基因表达图像中拟合细分网格。
DOI：
10.1007/978-3-642-33415-3_71
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Kakadiaris,IoannisA
中科院分区：
文献类型：
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作者：
Le,YenH;Kurkure,Uday;Paragios,Nikos;Ju,Tao;Carson,JamesP;Kakadiaris,IoannisA
图像中多部分解剖对象的自动分割是一项具有挑战性的任务。在本文中，我们提出了一个基于相似性的外观之前，以适应基因表达图像中的小鼠大脑的房室几何图谱。使用马尔可夫随机场（MRF）框架的细分网格，这是用来模拟的几何形状变形。所提出的外观先验被计算为来自两个图像的相应图谱位置处的局部补丁之间的相似性的函数。此外，我们引入了一个相似性显着性得分来选择网格点的计算建议的先验相关。我们的方法显着提高了图谱拟合的准确性，特别是在受选定的相似性显著点的影响的区域，并优于以前的细分网格拟合方法的基因表达图像。
Automated segmentation of multi-part anatomical objects in images is a challenging task. In this paper, we propose a similarity-based appearance-prior to fit a compartmental geometric atlas of the mouse brain in gene expression images. A subdivision mesh which is used to model the geometry is deformed using a Markov Random Field (MRF) framework. The proposed appearance-prior is computed as a function of the similarity between local patches at corresponding atlas locations from two images. In addition, we introduce a similarity-saliency score to select the mesh points that are relevant for the computation of the proposed prior. Our method significantly improves the accuracy of the atlas fitting, especially in the regions that are influenced by the selected similarity-salient points, and outperforms the previous subdivision mesh fitting methods for gene expression images.