The thickness of the stratum corneum (SC) is an important biomarker for assessing the quality of the skin barrier function. Thanks to LC-OCT imaging, it is now possible to determine SC thickness, in vivo, non-invasively and with a precision comparable to that of histology (about 1 \(\mu \) m). In this paper, we propose a reliable, unsupervised and fully automatic method for SC segmentation, incorporating hair removal. We compare it to the reference method for estimating SC thickness by LC-OCT based on deep learning model U-Net. Differences between the two methods are evaluated on a database of 900 images of healthy skin acquired from one hundred volunteers. The mean absolute error lies between 0.41 \(\mu \) m and 0.65 \(\mu \) m, i.e. around half the resolution of the imaging system. The strong similarity between the two methods is confirmed by a Student’s test on the hypothesis that the difference is less than the resolution of the imaging system, with p-value of less than 0.001. We can therefore conclude that our method is as efficient and accurate as the reference one using deep learning. Being unsupervised, our method has the advantage of not requiring manual annotation of a large dataset for model training. Furthermore, our method integrates hair removal in a fully automatic way, whereas U-Net segmentation must be followed by manual hair removal. These methods surpass the state-of-the-art of other imagery and could therefore help reduce the number of skin biopsies.