A robust segmentation method that can be used to perform measurements on toenails is presented. The proposed method is used as the first step in a clinical trial to objectively quantify the incidence of a particular pathology. For such an assessment, it is necessary to distinguish a nail, which locally appears to be similar to the skin. Many algorithms have been used, each of which leverages different aspects of toenail appearance. We used the Hough transform to locate the tip of the toe and estimate the nail location and size. Subsequently, we classified the super-pixels of the image based on their geometric and photometric information. Thereafter, the watershed transform delineated the border of the nail. The method was validated using a 348-image medical dataset, achieving an accuracy of 0.993 and an F-measure of 0.925. The proposed method is considerably robust across samples, with respect to factors such as nail shape, skin pigmentation, illumination conditions, and appearance of large regions affected by a medical condition

翻译：本文提出一种稳健的分割方法，可用于对趾甲进行测量。该方法作为临床试验的第一步，旨在客观量化特定病理的发生率。在此类评估中，需将局部形态与皮肤相似的指甲进行区分。现有多种算法被采用，各自利用趾甲外观的不同特征。本研究采用霍夫变换定位趾尖并估算指甲的位置与尺寸，继而基于超像素的几何与光度信息进行分类，最后通过分水岭变换勾勒指甲边界。该方法在包含348张图像的医学数据集上得到验证，准确率达0.993，F值达0.925。所提方法对指甲形状、皮肤色素沉着、光照条件及病症影响的大面积区域等干扰因素均表现出较强的样本鲁棒性。