Iterative voting for inference of structural saliency and characterization of subcellular events
Iterative voting for inference of structural saliency and characterization of subcellular events
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DOI：
10.1109/tip.2007.891154
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发表时间：
2007-03-01
影响因子：
10.6
通讯作者：
Barcellos-Hoff, Mary Helen
中科院分区：
文献类型：
--
作者：
Parvin, Bahram;Yang, Qing;Barcellos-Hoff, Mary Helen
显着性是在不同分辨率级别出现的重要感知线索。显着性的重要属性是对称性、连续性和封闭性。这些属性的检测常常受到噪声、尺度变化和不完整信息的阻碍。本文介绍了迭代投票方法，该方法使用定向核来推断与对称性相关的显着性。该技术的一个独特方面是内核拓扑，它会迭代地进行细化和重新定向。该技术可以沿着物体形状的径向线对非凸感知圆形对称性进行聚类和分组。它具有出色的抗噪性，并且能够耐受规模扰动。演示了该技术在通过各种显微镜模式获得的图像中的应用。此外，作为一个案例，该方法已应用于量化电离辐射后组蛋白 γ H2AX 磷酸化形成的核灶形成的动力学。迭代投票已在 2D 和 3D 中实现，用于多图像分析。
Saliency is an important perceptual cue that occurs at different levels of resolution. Important attributes of saliency are symmetry, continuity, and closure. Detection of these attributes is often hindered by noise, variation in scale, and incomplete information. This paper introduces the iterative voting method, which uses oriented kernels for inferring saliency as it relates to symmetry. A unique aspect of the technique is the kernel topography, which is refined and reoriented iteratively. The technique can cluster and group nonconvex perceptual circular symmetries along the radial line of an object's shape. It has an excellent noise immunity and is shown to be tolerant to perturbation in scale. The application of this technique to images obtained through various modes of microscopy is demonstrated. Furthermore, as a case example, the method has been applied to quantify kinetics of nuclear foci formation that are formed by phosphorylation of histone gamma H2AX following ionizing radiation. Iterative voting has been implemented in both 2-D and 3-D for multi image analysis.