A Multi-Scale Gradient Algorithm Based on Morphological Operators

1 Introduction The performance of a watershed-based image segmentation method depends largely on the algorithm used to compute the gradient. Conventional morphological gradient operators2~3] produce too many local minima because of noise and quantization…

A Multi-Scale Gradient Algorithm Based on Morphological Operators

Watershed transformation is a powerful morphological tool for image segmentation. However, the performance of the image segmentation methods based on watershed transformation depends largely on the algorithm for computing the gradient of the image to be segmented. In this paper, we present a multi-scale gradient algorithm based on morphological operators for watershed-based image segmentation, with effective handling of both step and blurred edges. We also present an algorithm to eliminate the local minima produced by noise and quantization errors. Experimental results indicate that watershed transformation with the algorithms proposed in this paper produces meaningful segmentations, even without a region-merging step.