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Exponential filters to extract small structures in noisy images

机译:指数过滤器可提取噪点图像中的小结构

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Segmentation of small structures in noisy images is inherently challenging because the edge information is contained in the same high frequency component as the noise. We have overcome this obstacle in the analysis of the sural nerve in the ankle by processing images to reduce noise and detecting edges with a detector which has reduced sensitivity to noise. Effective segmentation delineates the nerve boundary without breaking the nerve structure into sub-regions and was evaluated in two ways. Firstly, by visual comparison with specific anatomy in each of 40 subjects, comprising a partial population from a nerve hydration study. Secondly, by quantitative comparison with nerve hydration measurements obtained by previously described manual methods. The measurements obtained from this semi-automated approach show close correlation with those obtained manually.
机译:噪声图像中小结构的分割具有固有的挑战性,因为边缘信息包含在与噪声相同的高频分量中。通过处理图像以减少噪声并使用对噪声敏感度降低的检测器检测边缘,我们克服了踝部腓肠神经分析中的这一障碍。有效的分割划定了神经边界,而没有将神经结构破坏为子区域,并通过两种方式进行了评估。首先,通过视觉比较40位受试者中的每位受试者的特定解剖结构,这些受试者包括来自神经水化研究的部分人群。其次,通过与通过先前描述的手动方法获得的神经水化测量结果进行定量比较。从这种半自动化方法获得的测量结果与手动获得的测量结果密切相关。

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