论文标题
Bhattacharyya系数的基于噪声模型的随机沃克图像分段的框架
A Bhattacharyya Coefficient-Based Framework for Noise Model-Aware Random Walker Image Segmentation
论文作者
论文摘要
一种良好的交互式图像分割方法是随机Walker算法。近年来，对这种分割方法家族的大量研究一直在不断进行。这些方法在使用简单的高斯重量函数时很常见，该函数取决于强烈影响分割性能的参数。在这项工作中，我们提出了一个基于概率建模的重量功能的一般框架。可以将该框架结合起来以应对几乎任何定义明确的噪声模型。它消除了关键参数，从而避免了耗时的参数搜索。我们得出了普通噪声类型的特定权重函数，并在合成数据以及不同的生物医学图像数据（来自NYU FastMRI数据集的MRI图像，使用FIM技术中获取的幼虫图像）上显示出了出色的性能。我们的框架也可以用于多个其他应用程序中，例如图形切割算法及其扩展。
One well established method of interactive image segmentation is the random walker algorithm. Considerable research on this family of segmentation methods has been continuously conducted in recent years with numerous applications. These methods are common in using a simple Gaussian weight function which depends on a parameter that strongly influences the segmentation performance. In this work we propose a general framework of deriving weight functions based on probabilistic modeling. This framework can be concretized to cope with virtually any well-defined noise model. It eliminates the critical parameter and thus avoids time-consuming parameter search. We derive the specific weight functions for common noise types and show their superior performance on synthetic data as well as different biomedical image data (MRI images from the NYU fastMRI dataset, larvae images acquired with the FIM technique). Our framework can also be used in multiple other applications, e.g., the graph cut algorithm and its extensions.
