Konukoglu Ender, Glocker Ben, Zikic Darko, Criminisi Antonio
Microsoft Research Cambridge, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):75-82. doi: 10.1007/978-3-642-33454-2_10.
Methods that leverage neighbourhood structures in high-dimensional image spaces have recently attracted attention. These approaches extract information from a new image using its "neighbours" in the image space equipped with an application-specific distance. Finding the neighbourhood of a given image is challenging due to large dataset sizes and costly distance evaluations. Furthermore, automatic neighbourhood search for a new image is currently not possible when the distance is based on ground truth annotations. In this article we present a general and efficient solution to these problems. "neighbourhood approximation forests" (NAF) is a supervised learning algorithm that approximates the neighbourhood structure resulting from an arbitrary distance. As NAF uses only image intensities to infer neighbours it can also be applied to distances based on ground truth annotations. We demonstrate NAF in two scenarios: (i) choosing neighbours with respect to a deformation-based distance, and (ii) age prediction from brain MRI. The experiments show NAF's approximation quality, computational advantages and use in different contexts.
利用高维图像空间中的邻域结构的方法最近受到了关注。这些方法在配备了特定应用距离的图像空间中，使用新图像的“邻居”从新图像中提取信息。由于数据集规模庞大且距离评估成本高昂，找到给定图像的邻域具有挑战性。此外，当距离基于真实标注时，目前还无法对新图像进行自动邻域搜索。在本文中，我们提出了针对这些问题的通用且高效的解决方案。“邻域近似森林”（NAF）是一种监督学习算法，它近似由任意距离产生的邻域结构。由于NAF仅使用图像强度来推断邻居，因此它也可应用于基于真实标注的距离。我们在两种场景中展示了NAF：（i）基于基于变形的距离选择邻居，以及（ii）从脑部MRI进行年龄预测。实验展示了NAF的近似质量、计算优势以及在不同场景中的应用。
