Abstract
In this study, a segmentation method is presented for the images of microscopic peripheral blood which mainly contain red blood cells, some of which contain parasites, and some white blood cells. The method uses several operators based on mathematical morphology. The cell area information which is estimated using the area granulometry (area pattern spectrum) is used for several steps in the method. A modified version of the original watershed algorithm [31] called minimum area watershed transform is developed and employed as an initial segmentation operator. The circle Radon transform is applied to the labelled regions to locate the cell centers (markers). The final result is produced by applying the original marker controlled watershed transform to the Radon transform output with its markers obtained from the regional maxima. The proposed method can be applied to similar blob object segmentation problems by adapting red blood cell characteristics for the new blob objects. The method has been tested on a benchmark set and scored a successful correct segmentation rate of 95.40%.
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Tek, F.B., Dempster, A.G., Kale, I. (2005). Blood Cell Segmentation Using Minimum Area Watershed and Circle Radon Transformations. In: Ronse, C., Najman, L., Decencière, E. (eds) Mathematical Morphology: 40 Years On. Computational Imaging and Vision, vol 30. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3443-1_40
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DOI: https://doi.org/10.1007/1-4020-3443-1_40
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