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Automatic measurement of epidermal thickness from optical coherence tomography images using a new algorithm

机译:使用新算法从光学相干断层扫描图像自动测量表皮厚度

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Background/purpose: Optical coherence tomography (OCT) is an imaging system that enables in vivo epidermal thickness (ET) measurement. In order to use OCT in large-scale clinical studies, automatic algorithm detection of the dermo-epidermal junction (DEJ) is needed. This may be difficult due to image noise from optical speckle, which requires specific image treatment procedures to reduce this. In the present work, a description of the position of the DEJ is given, and an algorithm for boundary detection is presented. Methods: Twenty-nine images were taken from the skin of normal healthy subjects, from five different body sites. Seven expert assessors were asked to trace the DEJ for ET measurement on each of the images. The variability between experts was compared with a new image processing method. Results: Between-expert variability was relatively low with a mean standard deviation of 3.4mum. However, local positioning of the DEJ between experts was often different.The described algorithm performed adequately on all images. ET was automatically measured with a precision of <5mum compared with the experts on all sites studied except that of the back. Moreover, the local algorithm positioning was verified. Conclusion: The new image processing method for measuring ET from OCT images significantly reduces calculation time for this parameter, and avoids user intervention. The main advantages of this are that data can be analyzed more rapidly and reproducibly in clinical trials.
机译:背景/目的:光学相干断层扫描(OCT)是一种成像系统,可进行体内表皮厚度(ET)测量。为了在大型临床研究中使用OCT,需要对表皮-表皮交界处(DEJ)进行自动算法检测。由于来自光学散斑的图像噪声,这可能很困难,这需要特定的图像处理程序来减少。在本工作中,给出了DEJ位置的描述,并提出了一种边界检测算法。方法:从正常健康受试者的皮肤,五个不同的部位拍摄了29张图像。要求七名专家评估员在每个图像上追踪DEJ以进行ET测量。专家之间的差异与一种新的图像处理方法进行了比较。结果:专家之间的变异性相对较低,平均标准偏差为3.4μm。然而,专家之间DEJ的本地定位通常是不同的。所描述的算法在所有图像上都能充分执行。与所有研究地点的专家(除背面)相比,ET的自动测量精度均小于5um。此外,验证了局部算法的定位。结论:从OCT图像测量ET的新图像处理方法大大减少了该参数的计算时间,并且避免了用户干预。这样做的主要优点是可以在临床试验中更快,更重复地分析数据。

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