Abstract
In this paper, we present a novel segmentation framework for glandular structures in Hematoxylin and Eosin stained histology images, choosing poorly differentiated colon tissue as an example. The proposed framework’ target is to identify precise epithelial nuclei objects. We start with staining separate to detect all nuclei objects, and deploy multi-resolution morphology operation to map the initial epithelial nuclei positions. We proposed a new bag of words scheme using sparse random feature to classify epithelial nuclei and stroma nuclei objects to adjust the rest nuclei positions. Finally, we can use the boundary of optimized epithelial nuclei objects to segment the glandular structure.
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Acknowledgements
This work was financially supported by the Natural Science Foundation of Jiangsu Province, China under grant No. BK20170443. Nantong Research Program of Application Foundation under Grant No. GY12016022 and Dr. H Zhou is currently supported by UK EPSRC under Grant EP/N011074/1, and Newton Advanced Fellowship under Grant NA160342.
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Zhang, K., Zhou, H., Chen, L., Fei, M., Wu, J., Zhang, P. (2017). A Novel Segmentation Framework Using Sparse Random Feature in Histology Images of Colon Cancer. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_17
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DOI: https://doi.org/10.1007/978-981-10-6370-1_17
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