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Pyramides irrégulières descendantes pour la segmentation de grandes images histologiques

Romain Goffe 1 
XLIM - XLIM, Université de Poitiers
Abstract : Some data acquisition devices produce images of several gigabytes. Analyzing such large images raises two main issues. First, the data volume to process forbids a global image analysis, hence a hard partitioning problem. Second, a multi-resolution approach is required to extract global features at low resolution. For instance, regarding histological images, recent improvments in scanners' accuracy allow nowadays to examine cellular structures on the whole slide. However, produced images are up to 18 GB. Besides, considering a tissue as a particular layout of cells is a global information that is only available at low resolution. Thus, these images combine multi-scale and multi-resolution information. In this work, we define a topological and hierarchical model which is suitable for the segmentation of large images. Our work is based on the models of topological map and combinatorial pyramid. We introduce the tiled map model in order to encode the topology of large partitions and a hierarchical extension, the tiled top-down pyramid, to represent the duality between multi-scale and multi-resolution information. Finally, we propose an application of our model for the segmentation of large images in histology.
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Submitted on : Saturday, January 7, 2012 - 4:05:00 PM
Last modification on : Wednesday, October 20, 2021 - 3:22:24 AM
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  • HAL Id : tel-00657621, version 1



Romain Goffe. Pyramides irrégulières descendantes pour la segmentation de grandes images histologiques. Algorithme et structure de données [cs.DS]. Université de Poitiers, 2011. Français. ⟨tel-00657621⟩



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