Integration of fuzzy spatial relations in deformable models - Application to brain MRI segmentation
Integration of fuzzy spatial relations in deformable models - Application to brain MRI segmentation
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DOI：
10.1016/j.patcog.2006.02.022
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发表时间：
2006-08-01
影响因子：
8
通讯作者：
Bloch, Isabelle
中科院分区：
文献类型：
--
作者：
Colliot, Olivier;Camara, Oscar;Bloch, Isabelle
本文提出了一种通用框架，用于在可变形模型中集成基于空间关系的新型约束。在该方法中，空间关系被表示为图像空间的模糊子集，并作为新的外力纳入可变形模型中。介绍并讨论了由表示空间关系的模糊集构造外力的三种方法。然后，该框架被用于在磁共振图像(MRI)中分割大脑皮质下结构。提出了一种训练步骤来估计定义这些关系的主要参数。结果表明，在可变形模型中引入空间关系可以显著改善对比度低和边界模糊的结构的分割。(C)2006年模式识别学会。爱思唯尔有限公司出版。保留所有权利。
This paper presents a general framework to integrate a new type of constraints, based on spatial relations, in deformable models. In the proposed approach, spatial relations are represented as fuzzy subsets of the image space and incorporated in the deformable model as a new external force. Three methods to construct an external force from a fuzzy set representing a spatial relation are introduced and discussed. This framework is then used to segment brain subcortical structures in magnetic resonance images (MRI). A training step is proposed to estimate the main parameters defining the relations. The results demonstrate that the introduction of spatial relations in a deformable model can substantially improve the segmentation of structures with low contrast and ill-defined boundaries. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.