A weighted-ROC graph based metric for image segmentation evaluation

• The importance of each pixel is taken into account in the evaluating process.
• Weighted strategy is applied to the ROC graph.
• The problem of distorted evaluation on images with region imbalance is eliminated.
• The assessment results are more in line with the subjective evaluation results.
• The metric holds for both illumination images and non-uniform illumination images.

Evaluation of image segmentation algorithms is a crucial task in the image processing field. Generally, traditional objective evaluation measures, such as ME and JS, always give the same treatment to the object pixels and the background pixels in images, which is not reasonable in practical applications. To overcome this problem, a new objective evaluation metric based on the weighted-ROC graph is proposed in this paper. Considering that pixels in different positions may gain different importance, each pixel is given a weight based on its spatial information. The ROC (receiver operating characteristic) graph with weighting strategy is constructed to evaluate the performance of segmentation algorithms quantitatively. The proposed metric focuses on the segmented objects, which is similar to human visual system. Meanwhile, it reserves the robustness of ROC against the region imbalance. The experimental results on various images show that the proposed metric gives more reasonable evaluation results than other metrics.