Tunable tensor voting improves grouping of membrane-bound macromolecules

Leandro A. Loss, G. Bebis, B. Parvin
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引用次数: 2

Abstract

Membrane-bound macromolecules are responsible for structural support and mediation of cell-cell adhesion in tissues. Quantitative analysis of these macromolecules provides morphological indices for damage or loss of tissue, for example as a result of exogenous stimuli. From an optical point of view, a membrane signal may have nonuniform intensity around the cell boundary, be punctate or diffused, and may even be perceptual at certain locations along the boundary. In this paper, a method for the detection and grouping of punctate, diffuse curvilinear signals is proposed. Our work builds upon the tensor voting and the iterative voting frameworks to propose an efficient method to detect and refine perceptually interesting curvilinear structures in images. The novelty of our method lies on the idea of iteratively tuning the tensor voting fields, which allows the concentration of the votes only over areas of interest. We validate the utility of our system with synthetic and annotated real data. The effectiveness of the tunable tensor voting is demonstrated on complex phenotypic signals that are representative of membrane-bound macromolecular structures.
可调张量投票改善了膜结合大分子的分组
膜结合大分子负责组织中细胞-细胞粘附的结构支持和调解。这些大分子的定量分析为组织损伤或损失提供形态学指标,例如作为外源性刺激的结果。从光学角度来看,膜信号在细胞边界周围可能具有不均匀的强度,可能是点状的或弥漫性的,甚至在沿边界的某些位置可能是可感知的。本文提出了一种点状扩散曲线信号的检测与分组方法。我们的工作建立在张量投票和迭代投票框架的基础上,提出了一种有效的方法来检测和改进图像中感知上有趣的曲线结构。我们方法的新颖之处在于迭代调整张量投票场的思想,这允许只在感兴趣的区域集中投票。我们用综合和注释的真实数据验证了系统的实用性。可调张量投票的有效性证明了复杂的表型信号,是膜结合大分子结构的代表。
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