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Localization of a Linear Structure Object in Medical Images Based on Hidden Markov Model

机译:基于隐马尔可夫模型的医学图像中线性结构对象的本地化

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In this paper a novel approach to localization of a linear structure object (spine) in medical images using one-dimensional Hidden Markov Model is proposed. Feature sequence of a linear structure object is extracted by using a horizontal-line sampling window with fixed width along the central axis of the object in training phase and the model training is performed through maximum likelihood estimation provided by the Baum-Welch algorithm. A specific localization method derived from Viterbi algorithm is presented for determining a feature sequence in a test image, from -which the position of a linear structure object could be obtained. The use of heuristic information improves obviously the performance of localization and the computational complexity. The experiments demonstrate the effectiveness in localization application based on the simple feature expression and the proposed localization method
机译:本文提出了一种使用一维隐马尔可夫模型的医学图像中的线性结构对象(脊柱)定位的新方法。通过使用沿着训练阶段的对象的中心轴的具有固定宽度的水平线采样窗口来提取线性结构对象的特征序列,并且通过由BAUM-Welch算法提供的最大似然估计来执行模型训练。呈现从维特比算法导出的特定定位方法,用于确定测试图像中的特征序列 - 从可以获得线性结构对象的位置。启发式信息的使用显然提高了本地化的性能和计算复杂性。该实验证明了基于简单特征表达和所提出的定位方法的本地化应用中的有效性

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