Joint volumetric extraction and enhancement of vasculature from low-SNR 3-D fluorescence microscopy images.
Joint volumetric extraction and enhancement of vasculature from low-SNR 3-D fluorescence microscopy images.
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DOI：
10.1016/j.patcog.2016.09.031
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发表时间：
2017-03
影响因子：
8
通讯作者：
Xu X
中科院分区：
文献类型：
--
作者：
Almasi S;Ben-Zvi A;Lacoste B;Gu C;Miller EL;Xu X
为了同时克服光学成像的特点所带来的挑战，包括空间变化的信噪比（SNR）、散射光和非均匀照明等一系列伪影，我们开发了一种新的方法，直接从原始荧光显微镜图像中分割三维血管系统，消除了使用大多数分割技术所使用的预处理和后处理步骤，如去噪和分割细化。该方法包括初始化和约束恢复和增强两个阶段。初始化方法是完全自动化的，使用来自双尺度统计度量的特征，并产生对非均匀光照、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，通过对距离、局部强度梯度和中值测度形成的特征向量进行投票提取结构，从而达到分割的目的。通过对合成数据和真实数据的实验结果进行定性和定量分析，证明了该方法与目前最先进的增强分割方法相比的有效性。算法的简单性，免于先验的噪声概率信息，以及结构定义使该算法具有广泛的潜在应用范围，即结构复杂性显着使分割问题复杂化。
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 a priori probabilistic information about the noise, and structural definition gives this algorithm a wide potential range of applications where i.e. structural complexity significantly complicates the segmentation problem.