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Epidermal layer recognition of optical coherence tomography skin images

机译:光学相干断层扫描皮肤图像的表皮层识别

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Epidermal layer recognition is an effective way to diagnose the severity of psoriasis and other skin diseases. Optical coherence tomography (OCT) is a non-invasive imaging modality that acquires images of tissue in vivo and plays an important role in detecting skin diseases and assessing therapeutic effects. In this paper, a method for epidermal layer recognition of OCT skin images is proposed. Firstly, the skin surface is obtained based on the axial gradient map. Then the original image is flattened according to the estimated skin surface. And the potential region of the dermo-epidermal junction (DEJ) is obtained according to the mean column signal. Finally, the epidermal layer is identified by topology transmission and seed filling. The method can automatically segment the curved mastoid structure of the DEJ which is suitable for OCT images of skin with weakened signal intensity of dermis due to dermatitis or other diseases.
机译:表皮层识别是诊断牛皮癣和其他皮肤病的严重程度的有效途径。光学相干断层扫描(OCT)是一种非侵入性成像模态,获取体内组织的图像,并在检测皮肤病和评估治疗效果方面发挥重要作用。本文提出了一种表皮层识别OCT皮肤图像的表皮层识别方法。首先,基于轴向梯度图获得皮肤表面。然后根据估计的皮肤表面扁平地平坦化。并且根据平均柱信号获得Dermo-表皮结(DEJ)的潜在区域。最后,通过拓扑传递和种子填充来识别表皮层。该方法可以自动分割DEJ的弯曲乳突结构,该结构适用于皮肤的OCT图像,由于皮炎或其他疾病导致皮肤的信号强度弱化。

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