Abstract
We propose a method for targeted segmentation that identifies and delineates only those spatially-recurring objects that conform to specific geometrical, topological and appearance priors. By adopting a “tribes”-based, global genetic algorithm, we show how we incorporate such priors into a faithful objective function unconcerned about its convexity. We evaluated our framework on a variety of histology and microscopy images to segment potentially overlapping cells with complex topology. Our experiments confirmed the generality, reproducibility and improved accuracy of our approach compared to competing methods.
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Keywords
- Random Forest
- Cell Segmentation
- Image Segmentation Method
- Histopathology Image
- Transferable Belief Model
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Nosrati, M.S., Hamarneh, G. (2013). Segmentation of Cells with Partial Occlusion and Part Configuration Constraint Using Evolutionary Computation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40811-3_58
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DOI: https://doi.org/10.1007/978-3-642-40811-3_58
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