Almasi Sepideh, Ben-Zvi Ayal, Lacoste Baptiste, Gu Chenghua, Miller Eric L, Xu Xiaoyin
Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA.
Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
Pattern Recognit. 2017 Mar;63:710-718. doi: 10.1016/j.patcog.2016.09.031. Epub 2016 Sep 22.
To simultaneously overcome the challenges imposed by the nature of optical imaging characterized by a range of artifacts including space-varying signal to noise ratio (SNR), scattered light, and non-uniform illumination, we developed a novel method that segments the 3-D vasculature directly from original fluorescence microscopy images eliminating the need for employing pre- and post-processing steps such as noise removal and segmentation refinement as used with the majority of segmentation techniques. Our method comprises two initialization and constrained recovery and enhancement stages. The initialization approach is fully automated using features derived from bi-scale statistical measures and produces seed points robust to non-uniform illumination, low SNR, and local structural variations. This algorithm achieves the goal of segmentation via design of an iterative approach that extracts the structure through voting of feature vectors formed by distance, local intensity gradient, and median measures. Qualitative and quantitative analysis of the experimental results obtained from synthetic and real data prove the effcacy of this method in comparison to the state-of-the-art enhancing-segmenting methods. The algorithmic simplicity, freedom from having probabilistic information about the noise, and structural definition gives this algorithm a wide potential range of applications where structural complexity significantly complicates the segmentation problem.
为了同时克服光学成像特性带来的挑战，这些挑战包括一系列伪像，如空间变化的信噪比（SNR）、散射光和非均匀照明，我们开发了一种新颖的方法，可直接从原始荧光显微镜图像中分割三维脉管系统，无需采用大多数分割技术所使用的诸如噪声去除和分割细化等预处理和后处理步骤。我们的方法包括两个初始化以及约束恢复和增强阶段。初始化方法使用从双尺度统计量得出的特征实现完全自动化，并生成对非均匀照明、低信噪比和局部结构变化具有鲁棒性的种子点。该算法通过设计一种迭代方法来实现分割目标，该方法通过对由距离、局部强度梯度和中值度量形成的特征向量进行投票来提取结构。对从合成数据和真实数据获得的实验结果进行的定性和定量分析证明，与最先进的增强分割方法相比，该方法具有有效性。算法的简单性、无需关于噪声的概率信息以及结构定义，使得该算法在结构复杂性使分割问题显著复杂化的广泛潜在应用领域中具有应用价值。
