Dabbah M A, Graham J, Petropoulos I, Tavakoli M, Malik R A
Imaging Sciences and Biomedical Engineering (ISBE), The University of Manchester, Oxford Rd, Manchester M13 9PT, UK.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):300-7. doi: 10.1007/978-3-642-15705-9_37.
Corneal Confocal Microscopy (CCM) imaging is a non-invasive surrogate of detecting, quantifying and monitoring diabetic peripheral neuropathy. This paper presents an automated method for detecting nerve-fibres from CCM images using a dual-model detection algorithm and compares the performance to well-established texture and feature detection methods. The algorithm comprises two separate models, one for the background and another for the foreground (nerve-fibres), which work interactively. Our evaluation shows significant improvement (p approximately 0) in both error rate and signal-to-noise ratio of this model over the competitor methods. The automatic method is also evaluated in comparison with manual ground truth analysis in assessing diabetic neuropathy on the basis of nerve-fibre length, and shows a strong correlation (r = 0.92). Both analyses significantly separate diabetic patients from control subjects (p approximately 0).
角膜共焦显微镜（CCM）成像技术是一种用于检测、量化和监测糖尿病周围神经病变的非侵入性替代方法。本文提出了一种使用双模型检测算法从CCM图像中检测神经纤维的自动化方法，并将其性能与成熟的纹理和特征检测方法进行比较。该算法由两个独立的模型组成，一个用于背景，另一个用于前景（神经纤维），它们相互作用。我们的评估表明，与竞争方法相比，该模型在错误率和信噪比方面都有显著提高（p约为0）。在基于神经纤维长度评估糖尿病神经病变时，还将该自动化方法与手动真值分析进行了比较，结果显示两者具有很强的相关性（r = 0.92）。两种分析都能显著区分糖尿病患者和对照组（p约为0）。
