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Fibered fluorescence microscopy (FFM) of intra epidermal nerve fibers-translational marker for peripheral neuropathies in preclinical research: processing and analysis of the data

机译:表皮内神经纤维的纤维荧光显微镜(FFM)-临床前研究中周围神经病变的翻译标记:数据处理和分析

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Peripheral neuropathy can be caused by diabetes or AIDS or be a side-effect of chemotherapy. Fibered Fluorescence Microscopy (FFM) is a recently developed imaging modality using a fiber optic probe connected to a laser scanning unit. It allows for in-vivo scanning of small animal subjects by moving the probe along the tissue surface. In preclinical research, FFM enables non-invasive, longitudinal in vivo assessment of intra epidermal nerve fibre density in various models for peripheral neuropathies. By moving the probe, FFM allows visualization of larger surfaces, since, during the movement, images are continuously captured, allowing to acquire an area larger then the field of view of the probe. For analysis purposes, we need to obtain a single static image from the multiple overlapping frames. We introduce a mosaicing procedure for this kind of video sequence. Construction of mosaic images with sub-pixel alignment is indispensable and must be integrated into a global consistent image aligning. An additional motivation for the mosaicing is the use of overlapping redundant information to improve the signal to noise ratio of the acquisition, because the individual frames tend to have both high noise levels and intensity inhomogeneities. For longitudinal analysis, mosaics captured at different times must be aligned as well. For alignment, global correlation-based matching is compared with interest point matching.rnUse of algorithms working on multiple CPU's (parallel processor/cluster/grid) is imperative for use in a screening model.
机译:周围神经病变可以由糖尿病或艾滋病引起,或者是化学疗法的副作用。光纤荧光显微镜(FFM)是最近开发的一种成像方式,它使用连接到激光扫描单元的光纤探头。通过沿组织表面移动探针,可以对小型动物受试者进行体内扫描。在临床前研究中,FFM可以在各种模型中对周围神经病变进行无创纵向体内表皮神经纤维密度的体内评估。通过移动探针,FFM可以可视化较大的表面,因为在移动过程中会连续捕获图像,从而获得比探针的视野更大的区域。出于分析目的,我们需要从多个重叠的帧中获取单个静态图像。我们介绍了这种视频序列的拼接过程。具有亚像素对齐的马赛克图像的构建是必不可少的,并且必须集成到全局一致的图像对齐中。镶嵌的另一个动机是使用重叠的冗余信息来提高采集的信噪比,因为各个帧往往同时具有高噪声级别和强度不均匀性。对于纵向分析,必须同时对齐在不同时间捕获的镶嵌图。为了对齐,将基于全局相关性的匹配与兴趣点匹配进行比较。在筛选模型中必须使用在多个CPU(并行处理器/集群/网格)上运行的算法。

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