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METHOD FOR LEARNING-BASED OBJECT DETECTION IN CARDIAC MAGNETIC RESONANCE IMAGES
METHOD FOR LEARNING-BASED OBJECT DETECTION IN CARDIAC MAGNETIC RESONANCE IMAGES
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机译:心脏磁共振图像中基于学习的对象检测方法
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摘要
An automated method for detection of an object of interest in magnetic resonance (MR) two-dimensional (2-D) images wherein the images comprise gray level patterns, the method includes a learning stage utilizing a set of positive/negative training samples drawn from a specified feature space. The learning stage comprises the steps of estimating the distributions of two probabilities iP/i and iN/i are introduced over the feature space, P being associated with positive samples including said object of interest and N being associated with negative samples not including said object of interest; estimating parameters of Markov chains associated with all possible site permutations using said training samples; computing the best site ordering that maximizes the Kullback distance between iP/i and iN/i ; computing and storing the log-likelihood ratios induced by said site ordering; scanning a test image at different scales with a constant size window; deriving a feature vector from results of said scanning; and classifying said feature vector based on said best site ordering.
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机译:一种用于在磁共振(MR)二维(2-D)图像中检测感兴趣对象的自动方法,其中图像包括灰度模式,该方法包括一个学习阶段,该阶段利用从中得出的一组正/负训练样本指定的特征空间。学习阶段包括以下步骤:估计在特征空间上引入两个概率 P i>和 N i>的分布,P与包括所述感兴趣对象和N的正样本相关联与不包括所述感兴趣对象的阴性样品相关联;使用所述训练样本估计与所有可能的位点排列有关的马尔可夫链的参数;计算最佳站点排序,使 P i>和 N i>之间的Kullback距离最大化;计算和存储由所述站点排序引起的对数似然比;使用恒定大小的窗口以不同的比例扫描测试图像;从所述扫描的结果得出特征向量;并基于所述最佳站点排序对所述特征向量进行分类。
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