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首页> 外文会议>International conference on graphic and image processing >Label Fusion Based Brain MR Image Segmentation via a Latent Selective Model
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Label Fusion Based Brain MR Image Segmentation via a Latent Selective Model

机译：通过潜在选择模型基于标签融合的脑MR图像分割

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Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based techniques have become the two principal branches of label fusion. However, these generative models and patch-based techniques are only loosely related, and the requirement for higher accuracy, faster segmentation, and robustness is always a great challenge. In this paper, we propose novel algorithm that combines the two branches using global weighted fusion strategy based on a patch latent selective model to perform segmentation of specific anatomical structures for human brain magnetic resonance (MR) images. In establishing this probabilistic model of label fusion between the target patch and patch dictionary, we explored the Kronecker delta function in the label prior, which is more suitable than other models, and designed a latent selective model as a membership prior to determine from which training patch the intensity and label of the target patch are generated at each spatial location. Because the image background is an equally important factor for segmentation, it is analyzed in label fusion procedure and we regard it as an isolated label to keep the same privilege between the background and the regions of interest. During label fusion with the global weighted fusion scheme, we use Bayesian inference and expectation maximization algorithm to estimate the labels of the target scan to produce the segmentation map. Experimental results indicate that the proposed algorithm is more accurate and robust than the other segmentation methods.
机译：多图谱分割是一种有效的方法，在自动标记医学图像中感兴趣的对象方面越来越受欢迎。最近，基于生成模型和基于补丁的技术的分割方法已成为标签融合的两个主要分支。但是，这些生成模型和基于补丁的技术只是松散相关，因此对更高的准确性，更快的分割速度和鲁棒性的要求始终是一个巨大的挑战。在本文中，我们提出了一种新颖的算法，该算法基于补丁潜在选择模型，使用全局加权融合策略结合两个分支，以对人脑磁共振（MR）图像进行特定解剖结构的分割。在建立目标补丁与补丁字典之间标签融合的概率模型时，我们探索了标签优先级中的Kronecker delta函数，该函数比其他模型更适合，并在确定从哪个训练开始之前设计了一个潜在的选择性模型作为成员资格。在每个空间位置生成目标补丁的强度和标签。由于图像背景是分割同样重要的因素，因此在标签融合过程中对其进行分析，我们将其视为独立的标签，以在背景和感兴趣区域之间保持相同的特权。在使用全局加权融合方案进行标签融合的过程中，我们使用贝叶斯推断和期望最大化算法来估计目标扫描的标签以生成分割图。实验结果表明，与其他分割方法相比，该算法具有更高的准确性和鲁棒性。

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