Point set morphological filtering and semantic spatial configuration modeling: Application to microscopic image and bio-structure analysis
Point set morphological filtering and semantic spatial configuration modeling: Application to microscopic image and bio-structure analysis
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DOI：
10.1016/j.patcog.2012.01.021
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发表时间：
2012-08-01
影响因子：
8
通讯作者：
Racoceanu, Daniel
中科院分区：
文献类型：
--
作者：
Lomenie, Nicolas;Racoceanu, Daniel
高层次的空间关系和配置建模问题在图像分析和模式识别领域中越来越受到重视。特别是，它被认为是重要的，当一个需要挖掘高内容的图像或大规模的图像数据库中的一个更有表现力的方式比一个纯粹的统计。继续以前的努力，通过开发特定的高效的形态学工具，如Delaunay三角网格表示，将结构分析，我们建议正式的空间关系建模技术，致力于无组织的点集。我们提供了一个原始的网格网格框架，这是更方便的结构表示的大型图像数据的兴趣点集和形态分析。所设计的数值运算符的集合基于特定的膨胀运算符，该膨胀运算符使得可以处理诸如图像数据的稀疏表示（诸如图形）上的“之间”或“左边”之类的概念。基于这个新的理论框架推理的图像，我们能够处理高层次的查询大型组织病理学图像，知道数字化的组织病理学是一个新的挑战，在生物成像领域，由于这些图像的高内容的性质和大尺寸。(C)2012爱思唯尔有限公司保留所有权利。
High-level spatial relation and configuration modeling issues are gaining momentum in the image analysis and pattern recognition fields. In particular, it is deemed important whenever one needs to mine high-content images or large scale image databases in a more expressive way than a purely statistically one. Continuing previous efforts to incorporate structural analysis by developing specific efficient morphological tools performing on mesh representations like Delaunay triangulations, we propose to formalize spatial relation modeling techniques dedicated to unorganized point sets. We provide an original mesh lattice framework which is more convenient for structural representations of large image data by means of interest point sets and their morphological analysis. The set of designed numerical operators is based on a specific dilation operator that makes it possible to handle concepts like "between" or "left of" over sparse representations of image data such as graphs. Based on this new theoretical framework for reasoning about images, we are able to process high-level queries over large histopathological images, knowing that digitized histopathology is a new challenge in the field of bio-imaging due to the high-content nature and large size of these images. (C) 2012 Elsevier Ltd. All rights reserved.