基于几何特征的脚趾甲分割方法，精准区分指甲与皮肤
Geometric-Based Nail Segmentation for Clinical Measurements
- 结合霍夫变换定位脚趾尖，再用超像素分类与分水岭分割
- 在348张医学图像上准确率达0.993，F-measure为0.925
- 适用于形状、肤色、光照及病变区域各异的复杂情况
提出一种稳健的分割方法，用于临床测量脚趾甲。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。由于指甲局部外观与皮肤相似，难以区分。现有算法利用指甲外观不同特征，本研究采用霍夫变换定位脚趾尖并估计指甲位置与大小，随后基于几何与光度信息对图像超像素进行分类，再通过分水岭变换精确勾勒指甲边界。方法在包含348张图像的医学数据集上验证，准确率为0.993，F-measure为0.925。该方法对指甲形状、皮肤色素、光照条件及大面积病变区域具有强鲁棒性。
原文摘要 · Abstract (English)
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
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