Geometric-based nail segmentation for clinical measurements

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.

Automated summary

This article presents a robust segmentation method for measuring toenails. The method is used in a clinical trial to objectively quantify the incidence of a specific pathology. It uses the Hough transform to locate the tip of the toe and estimate the nail location and size, and then classifies the super-pixels based on their geometric and photometric information. The watershed transform is then used to delineate the border of the nail.