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首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >Fast bias field reduction by localized Lloyd-Max quantization
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Fast bias field reduction by localized Lloyd-Max quantization

机译：通过局部Lloyd-Max量化快速减少偏置场

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摘要

Bias field reduction is a common problem in medical imaging. A bias field usually manifests itself as a smooth intensity variation across the image. The resulting image inhomogeneity is a severe problem for posterior image processing and analysis techniques such as registration or segmentation. In this paper, we present a fast debiasing technique based on localized Lloyd-Max quantization. Thereby, the local bias is modelled as a multiplicative field and is assumed to be slowly varying. The method is based on the assumption that the local, undegraded histogram is characterized by a limited number of gray values. The goal is then to find the discrete intensity values such that spreading those values according to the local bias field reproduces the global histogram as good as possible. We show that our method is capable of efficiently reducing (even strong) bias fields in 3D volumes in only a few seconds.
机译：偏置场减小是医学成像中的常见问题。偏置场通常表现为整个图像上平滑的强度变化。对于诸如配准或分割之类的后图像处理和分析技术，所产生的图像不均匀性是一个严重的问题。在本文中，我们提出了一种基于局部Lloyd-Max量化的快速去偏技术。因此，局部偏差被建模为一个乘性场，并被假定为缓慢变化。该方法基于这样的假设：局部的，未退化的直方图的特征在于数量有限的灰度值。然后，目标是找到离散的强度值，以便根据局部偏差场扩展这些值可以尽可能好地再现全局直方图。我们证明了我们的方法能够在短短几秒钟内有效减少3D体积中的偏置场（甚至强）。

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