Srivastava Ruchir, Yow Ai Ping, Cheng Jun, Wong Damon W K, Tey Hong Liang
Institute for Infocomm Research, 1 Fusionopolis Way, No. 21-01 Connexis (South Tower), 138632, Singapore.
Cixi Institute of Biomedical Engineering, Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, 1219 Zhongguan West Road, Zhenhai District, Ningbo 315201, China.
Biomed Opt Express. 2018 Jul 6;9(8):3590-3606. doi: 10.1364/BOE.9.003590. eCollection 2018 Aug 1.
Automatic skin layer segmentation in optical coherence tomography (OCT) images is important for a topographic assessment of skin or skin disease detection. However, existing methods cannot deal with the problem of shadowing in OCT images due to the presence of hair, scales, etc. In this work, we propose a method to segment the topmost layer of the skin (or the skin surface) using 3D graphs with a novel cost function to deal with shadowing in OCT images. 3D graph cuts use context information across B-scans when segmenting the skin surface, which improves the segmentation as compared to segmenting each B-scan separately. The proposed method reduces the segmentation error by more than 20% as compared to the best performing related work. The method has been applied to roughness estimation and shows a high correlation with a manual assessment. Promising results demonstrate the usefulness of the proposed method for skin layer segmentation and roughness estimation in both normal OCT images and OCT images with shadowing.
光学相干断层扫描（OCT）图像中的皮肤层自动分割对于皮肤的地形评估或皮肤疾病检测至关重要。然而，由于存在毛发、鳞屑等，现有方法无法处理OCT图像中的阴影问题。在这项工作中，我们提出了一种使用具有新颖代价函数的3D图来分割皮肤最上层（或皮肤表面）的方法，以处理OCT图像中的阴影。3D图割在分割皮肤表面时使用跨B扫描的上下文信息，与单独分割每个B扫描相比，这提高了分割效果。与性能最佳的相关工作相比，所提出的方法将分割误差降低了20%以上。该方法已应用于粗糙度估计，并与人工评估显示出高度相关性。有前景的结果证明了所提出的方法在正常OCT图像和有阴影的OCT图像中进行皮肤层分割和粗糙度估计的有用性。
