Performance measure characterization for evaluating neuroimage segmentation algorithms

Characterizing the performance of segmentation algorithms in brain images has been a persistent challenge due to the complexity of neuroanatomical structures, the quality of imagery and the requirement of accurate segmentation. There has been much interest in using the Jaccard and Dice similarity coefficients associated with Sensitivity and Specificity for evaluating the performance of segmentation algorithms. This paper addresses the essential characteristics of the fundamental performance measure coefficients adopted in evaluation frameworks. While exploring the properties of the Jaccard, Dice and Specificity coefficients, we propose new measure coefficients Conformity and Sensibility for evaluating image segmentation techniques. It is indicated that Conformity is more sensitive and rigorous than Jaccard and Dice in that it has better discrimination capabilities in detecting small variations in segmented images. Comparing to Specificity, Sensibility provides consistent and reliable evaluation scores without the incorporation of image background properties. The merits of the proposed coefficients are illustrated by extracting neuroanatomical structures in a wide variety of brain images using various segmentation techniques.