4 April 1997 Segmentation of microscopic images by flooding simulation: a catchment-basins merging algorithm
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This work addresses a catchment basins merging algorithm developed to automate the segmentation of microscopic images, which is directly derived from the traditional non- hierarchical watershed algorithm. The proposed merging algorithm, based on digital topology concepts, employs regional criteria to merge the non-significant minima. It can be classified as a region growing method by flooding simulation, working at the scale of the main structures. The shape of the structures is absolutely irrelevant to the merging process. As a characteristic of the flooding simulation methods, the gray level image is viewed as a relief where each gray level is assigned a height. In the proposed method the relief is always flooded from all its local minima which are progressively detected and merged as the flooding takes place. The catchment basins merging process is guided by two parameters: a depth criterion and an area criterion. This solution suppresses the characteristic over-segmentation of the traditional watershed enabling the direct segmentation of the original image without the need of a previous pre-processing step. Due to the automatic detection of all local minima there is not need of the explicit marker extraction step often required by other flooding simulation methods. It is shown that this solution produces excellent segmentation results allowing the characterization of several materials from their microscopic images.
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Marcos Carneiro de Andrade, Gilles Bertrand, Arnoldo de Albuquerde Araujo, "Segmentation of microscopic images by flooding simulation: a catchment-basins merging algorithm", Proc. SPIE 3026, Nonlinear Image Processing VIII, (4 April 1997); doi: 10.1117/12.271119; https://doi.org/10.1117/12.271119

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