Tiled top-down pyramids and segmentation of large histological images

International audienceRecent microscopic imaging systems such as whole slide scanners provide very large (up to 18GB) high resolution images. Such amounts of memory raise major issues that prevent usual image representation models from being used. Moreover, using such high resolution images, global image features, such as tissues, do not clearly appear at full resolution. Such images contain thus different hierarchical information at different resolutions. This paper presents the model of tiled top-down pyramids which provides a framework to handle such images. This model encodes a hierarchy of partitions of large images defined at different resolutions. We also propose a generic construction scheme of such pyramids whose validity is evaluated on an histological image application

Pyramides irrégulières descendantes pour la segmentation de grandes images histologiques

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.Différents modes d'acquisition permettent d'obtenir des images de plusieurs gigaoctets. L'analyse de ces grandes images doit faire face à deux problèmes majeurs. Premièrement, le volume de données à traiter ne permet pas une analyse globale de l'image, d'où la difficulté d'en construire une partition. Deuxièmement, une approche multi-résolution est nécessaire pour distinguer les structures globales à faible résolution. Par exemple, dans le cadre des images d'histologie, les récentes améliorations des scanners permettent d'observer les structures cellulaires sur l'ensemble de la lame. En contrepartie, les images produites représentent jusqu'à 18 Go de données. De plus, l'agencement de ces cellules en tissus correspond à une information globale qui ne peut être observée qu'à faible résolution. Ces images combinent donc un aspect multi-échelle et multi-résolution. Dans ce manuscrit, nous définissons un modèle topologique et hiérarchique adapté à la segmentation de grandes images. Nos travaux sont fondés sur les modèles existants de carte topologique et de pyramide combinatoire. Nous présentons le modèle de carte tuilée pour la représentation de grandes partitions ainsi qu'une extension hiérarchique, la pyramide descendante tuilée, qui représente la dualité des informations multi-échelle et multi-résolution. Enfin, nous utilisons notre modèle pour la segmentation de grandes images en histologie

Tiled top-down combinatorial pyramids for large images representation

International audienceThe uprising number of applications that involve very large images with resolutions greater than 30\,000$\times$30\,000 raises major memory management issues. Firstly, the amount of data usually prevents such images from being processed globally and therefore, designing a global image partition raises several issues. Secondly, a multi-resolution approach is necessary since an analysis only based on the highest resolution may miss global features revealed at lower resolutions. This paper introduces the tiled top-down pyramidal framework which addresses these two main constraints. Our model provides a full representation of multi-resolution images with both geometrical and topological relationships. The advantage of a top-down construction scheme is twofold: the focus of attention only refines regions of interest which results in a reduction of the amount of required memory and in a refinement process that may take into account hierarchical features from previous segmentations. Moreover, the top-down model is combined with a decomposition in tiles to provide an accurate memory bounding while allowing global analysis of large images

Extraction of tiled top-down irregular pyramids from large images

Processing large images is a common issue in the computer vision framework with applications such as satellite or microscopic images. The major problem comes from the size of those images that prevents them from being loaded globally into memory. Moreover, such images contain different information at different levels of resolution. For example, global features, such as the delimitation of a tissue, appear at low resolution whereas finer details, such as cells, can only be distinguished at full resolution. Thus, the objective of this paper is the definition of a suitable hierarchical data structure that would provide full access to all the properties of the image by representing topological information. The idea consists in transposing the notion of tile for top-down topological pyramids to control accurately the amount of memory required by the construction of our model. As a result, this paper defines the topological model of tiled top-down pyramid and proposes a construction scheme that would not depend on the system memory limitations

Pyramides irrégulières descendantes pour la segmentation de grandes images histologiques

Différents modes d'acquisition permettent d'obtenir des images de plusieurs gigaoctets. L'analyse de ces grandes images doit faire face à deux problèmes majeurs. Premièrement, le volume de données à traiter ne permet pas une analyse globale de l'image, d'où la difficulté d'en construire une partition. Deuxièmement, une approche multi-résolution est nécessaire pour distinguer les structures globales à faible résolution. Par exemple, dans le cadre des images d'histologie, les récentes améliorations des scanners permettent d'observer les structures cellulaires sur l'ensemble de la lame. En contrepartie, les images produites représentent jusqu à 18 Go de données. De plus, l'agencement de ces cellules en tissus correspond à une information globale qui ne peut être observée qu'à faible résolution. Ces images combinent donc un aspect multi-échelle et multi-résolution. Dans ce manuscrit, nous définissons un modèle topologique et hiérarchique adapté à la segmentation de grandes images. Nos travaux sont fondés sur les modèles existants de carte topologique et de pyramide combinatoire. Nous présentons le modèle de carte tuilée pour la représentation de grandes partitions ainsi qu'une extension hiérarchique, la pyramide descendante tuilée, qui représente la dualité des informations multi-échelle et multi-résolution. Enfin, nous utilisons notre modèle pour la segmentation de grandes images en histologie.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.POITIERS-BU Sciences (861942102) / SudocSudocFranceF

A top down construction scheme for irregular pyramids

Hierarchical data structures such as irregular pyramids are used by many applications related to image processing and segmentation. The construction scheme of such pyramids is bottom-up. Such a scheme forbids the definition of a level according to more global information defined at upper levels in the hierarchy. Moreover, the base of the pyramid has to encode any single pixel of the initial image in order to allow the definition of regions of any shape at higher levels. This last constraint raises major issues of memory usage and processing costs when irregular pyramids are applied to large images. The objective of this paper is to define a top-down construction scheme for irregular pyramids. Each level of such a pyramid is encoded by a combinatorial map associated to an explicit encoding of the geometry and the inclusion relationships of the corresponding partition. The resulting structure is a stack of finer and finer partitions obtained by successive splitting operations and is called a top-down pyramid