Heterogeneous stacking for classification-driven watershed segmentation
EURASIP Journal on Advances in Signal Processing
Mobile support for diagnosis of communicable diseases in remote locations
Proceedings of the 13th International Conference of the NZ Chapter of the ACM's Special Interest Group on Human-Computer Interaction
Malaria Parasite Detection: Automated Method Using Microscope Color Image
International Journal of E-Health and Medical Communications
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In this paper we study the ability of the cooperation of Bayesian color pixel classification in extracting seeds for color watershed. Using color pixel classification alone does not extract accurately enough color regions so we suggest to use a strategy based on three steps: simplification, Bayesian classification and color watershed. Color watershed is based on an aggregation function using local and global criteria. The strategy is performed on microscopicimages. Quantitative measures are used to evaluate the resulting segmentations according to a set of reference images.