Optimized Weighted Performance Index for Objective Evaluation of Border-Detection Methods in Dermoscopy Images

Abstract

Quantitative evaluation of the existing border-detection methods is commonly performed by using different metrics. This is inherently problematic due to the different characteristics of each metric. This paper presents a novel approach for objective evaluation of border-detection methods in dermoscopy images by introducing a comprehensive evaluation metric: optimized weighted performance index. The index is formed as a nonlinear weighted function of the six commonly used metrics of sensitivity, specificity, accuracy, precision, border error, and similarity. Constrained nonlinear multivariable optimization techniques are applied to determine the optimal set of weights that result in the maximum value of the index. This index is used as an effective measure of the value of a given border-detection method and, thus, provides a basis for comparison with other methods. To demonstrate the effectiveness of the proposed index, it is used to evaluate five recent border-detection methods applied on a set of 55 high-resolution dermoscopy images.