Geometric-Based Nail Segmentation for Clinical Measurements

TL;DR

This paper introduces a robust geometric-based segmentation method for toenails, combining Hough transform, super-pixel classification, and watershed transform, validated on a medical dataset with high accuracy and robustness.

Contribution

It presents a novel combination of geometric and photometric techniques for toenail segmentation, improving accuracy and robustness over existing methods.

Findings

Achieved 0.993 accuracy in toenail segmentation

F-measure of 0.925 indicates high segmentation quality

Method is robust across various nail shapes and conditions

Abstract

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