IEEE Trans Image Process. 2013 Nov;22(11):4394-406. doi: 10.1109/TIP.2013.2273666.
This paper devises an augmented active surface model for the recovery of small structures in a low resolution and high noise setting, where the role of regularization is especially important. The emphasis here is on evaluating performance using real clinical computed tomography (CT) data with comparisons made to an objective ground truth acquired using micro-CT. In this paper, we show that the application of conventional active contour methods to small objects leads to non-optimal results because of the inherent properties of the energy terms and their interactions with one another. We show that the blind use of a gradient magnitude based energy performs poorly at these object scales and that the point spread function (PSF) is a critical factor that needs to be accounted for. We propose a new model that augments the external energy with prior knowledge by incorporating the PSF and the assumption of reasonably constant underlying CT numbers.
本文提出了一种增强的主动表面模型，用于在低分辨率和高噪声环境下恢复小结构，其中正则化的作用尤为重要。本文重点使用真实的临床计算机断层扫描（CT）数据评估性能，并与使用微 CT 获取的客观真实数据进行比较。在本文中，我们表明，由于能量项的固有特性及其相互作用，将传统的主动轮廓方法应用于小目标会导致非最优结果。我们表明，在这些物体尺度上，盲目使用基于梯度幅度的能量会表现不佳，而点扩散函数（PSF）是一个需要考虑的关键因素。我们提出了一种新的模型，通过将 PSF 和合理的恒定量 CT 数假设纳入外部能量，从而增强了先验知识。
