Sparse MRF Appearance Models for Fast Anatomical Structure Localisation
Sparse MRF Appearance Models for Fast Anatomical Structure Localisation
复制标题
用于快速解剖结构定位的稀疏 MRF 外观模型
DOI：
10.5244/c.21.109
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
H. Bischof
中科院分区：
文献类型：
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作者：
R. Donner;Branislav Micusík;G. Langs;H. Bischof
像活动形状模型、活动外观模型或蛇这样的图像分割方法需要初始化，以保证与要分割的对象有相当大的重叠。本文提出了一种利用马尔可夫随机场（MRF）对解剖结构进行全局定位的方法。它不需要初始化，而是在图像中找到查询结构最合理的匹配。它提供了精确、可靠和快速的结构检测，可以作为更详细的分割步骤的初始化。稀疏MRF外观模型（SAMs）对感兴趣点的几何结构、这些点的局部特征以及邻近点边缘的局部特征进行先验信息编码。该信息用于形成马尔可夫随机场，并通过MAX-SUM算法将建模对象（例如椎骨序列）映射到查询图像兴趣点。局部图像信息由基于对称的兴趣点和基于梯度向量流的局部描述符捕获。在两个数据集上的实验结果显示了该方法对复杂医学数据的适用性。
Image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an approach that localises anatomical structures in a global manner by means of Markov Random Fields (MRF). It does not need initialisation, but finds the most plausible match of the query structure in the image. It provides for precise, reliable and fast detection of the structure and can serve as initialisation for more detailed segmentation steps. Sparse MRF Appearance Models (SAMs) encode a priori information about the geometric configurations of interest points, local features at these points and local features along the edges of adjacent points. This information is used to formulate a Markov Random Field and the mapping of the modeled object (e.g. a sequence of vertebrae) to the query image interest points is performed by the MAX-SUM algorithm. The local image information is captured by novel symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.