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首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >A New Distribution Metric for Image Segmentation
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A New Distribution Metric for Image Segmentation

机译：一种新的图像分割分布指标

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摘要

In this paper, we present a new distribution metric for image segmentation that arises as a result in prediction theory. Forming a natural geodesic, our metric quantifies "distance" for two density functionals as the standard deviation of the difference between logarithms of those distributions. Using level set methods, we incorporate an energy model based on the metric into the Geometric Active Contour framework. Moreover, we briefly provide a theoretical comparison between the popular Fisher Information metric, from which the Bhattacharyya distance originates, with the newly proposed similarity metric. In doing so, we demonstrate that segmentation results are directly impacted by the type of metric used. Specifically, we qualitatively compare the Bhattacharyya distance and our algorithm on the Kaposi Sarcoma, a pathology that infects the skin. We also demonstrate the algorithm on several challenging medical images, which further ensure the viability of the metric in the context of image segmentation.
机译：在本文中，我们提出了一种新的用于图像分割的分布度量，该度量是预测理论的结果。形成自然的测地线时，我们的度量将两个密度函数的“距离”量化为那些分布的对数之间的差的标准偏差。使用水平集方法，我们将基于度量的能量模型合并到Geometric Active Contour框架中。此外，我们简要地提供了流行的Fisher信息量度（该数据来自Bhattacharyya距离）与新提出的相似性量度之间的理论比较。通过这样做，我们证明了细分结果直接受到所用度量类型的影响。具体来说，我们定性地比较了Bhattacharyya距离和我们的算法在感染皮肤的病理性卡波西肉瘤中的作用。我们还将在几种具有挑战性的医学图像上演示该算法，从而进一步确保度量在图像分割的背景下的可行性。

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