Geometric-Based Nail Segmentation for Clinical Measurements
This work addresses the need for robust toenail segmentation in clinical trials to quantify pathology incidence, though it appears incremental as it builds on existing geometric and photometric methods.
The paper tackles the problem of segmenting toenails from skin in medical images to enable objective clinical measurements of pathology, achieving an accuracy of 0.993 and an F-measure of 0.925 on a 348-image dataset.
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