Applying graphical oracles to evaluate image segmentation results

Abstract

Segmentation plays an important role in the pattern recognition and image processing areas. Several techniques have been proposed aiming at solving generic issues or particular applications. Traditionally, these techniques have been evaluated by using the Overlap measure, which verifies the coincident and non-coincident areas between the image resulting from a segmentation process and an image considered correct. Albeit widely, this type of measure does not allow flexibility in the assessment process. We here propose an approach to evaluate segmentation techniques using concepts from content-based image retrieval and considering a methodology for testing generic programs with graphical outputs, named graphic oracle. Our approach was applied to evaluate the segmentation of mammographic images, and the results indicate a performance compatible with the traditional measure with more flexibility and precision. Thus, our approach provides a contribution to allow a more flexible segmentation assessment, according to image characteristics and application objectives.