Robust watershed segmentation using wavelets

The use of watersheds in image segmentation relies mostly on a good estimation of image gradients. However, background noise tends to produce spurious gradients, causing over-segmentation and degrading the result of the watershed transform. Also, low-contrast edges generate small magnitude gradients, causing distinct regions to be erroneously merged. In this paper, a new technique is presented to improve the robustness of the segmentation using watersheds, which attenuates the over-segmentation problem. A redundant wavelet transform is used to de-noise the image, enhance edges in multiple resolutions, and obtain an enhanced version of image gradients. Then, the watershed transform is applied to the obtained gradient image, and the segmented regions that do not satisfy specific criteria are removed or merged. Applications of our segmentation approach to noisy and/or blurred images are discussed, emphasizing a case study in fingerprint segmentation.