Image segmentation using thick-fluid watersheds

Traditional watershed and marker-based image segmentation algorithms are very sensitive to noise. The main reason for this is that these segmentation algorithms are locally dependent on some type of edge indicator input image that is traditionally computed on a pixel-by-pixel basis. Additionally, as a result of raw watershed segmentation, the original image can be seriously oversegmented, and it may be difficult to reduce the oversegmentation and the impact of noise without also inducing several undesired region merges. This last problem is a typical result of local "edge gaps" that may appear along the topographic watershed mountain rims. Through these gaps the marker or watershed labels can easily leak into neighboring segments. We propose a novel pair of algorithms that uses "thick fluid" label propagation in order to try and solve these problems. The thick fluid technique is based on considering information from multiple adjacent pixels along the topographic watershed mountain rims that separate the different objects in an initial pre-segmented image.