Seeded Segmentation Methods for Medical Image Analysis

Segmentation is one of the key tools in medical image analysis. The objective of segmentation is to provide reliable, fast, and effective organ delineation. While traditionally, particularly in computer vision, segmentation is seen as an early vision tool used for subsequent recognition, in medical imaging the opposite is often true. Recognition can be performed interactively by clinicians or automatically using robust techniques, while the objective of segmentation is to precisely delineate contours and surfaces. This can lead to effective techniques known as “intelligent scissors” in 2D and their equivalent in 3D. This chapter is divided as follows. Section 3.1 starts off with a more “philosophical” section setting the background for this study. We argue for a segmentation context where high-level knowledge, object information, and segmentation method are all separate. In Sect. 3.2, we survey in some detail a number of segmentation methods that are well-suited to image analysis, in particular of medical images. We illustrate this, make some comparisons and some recommendations. In Sect. 3.3, we introduce very recent methods that unify many popular discrete segmentation methods and we introduce a new technique. In Sect. 3.4, we give some remarks about recent advances in seeded, globally optimal active contour methods that are of interest for this study. In Sect. 3.5, we compare all presented methods qualitatively. We then conclude and give some indications for future work.