ON SUPERVISED METRICS FOR SHAPE SEGMENTATION

Segmentation is one of the most critical steps in image analysis. Also, the quantification of the error commited during this step is not a straightforward task. In this work, the performance of some comparison function or metrics are studied, when just one object appears in the analyzed regions. We develop a method for rank many validation measures of segmentation algorithms. It is based on thresholding a test image with a range of threshold and to find the middle threshold value when the performance measure is minimum or maximum. The performance is plotted and the first derivate is employed in the ranking construction. We have determined that RDE and MHD are two performance measures that show the best results (both are the most selective).