Hyperspectral image segmentation: the butterfly approach

International audienceFew methods are proposed in the litterature for coupling the spectral and the spatial dimension available on hyperspectral images. This paper proposes a generic segmentation scheme named butterfly based on an iterative process and a cross analysis of spectral and spatial information. Indeed, spatial and spatial structures are extracted in spatial and spectral space respectively both taking into account the other one. To apply this layout on hyperspectral imgages, we focus particulary on spatial and spectral structures i.e. topologic concepts and latent variable for the spatial and the spectral space respectively. Moreover, a cooperation scheme with these structures is proposed. Finally, results obtained on real hyperspectral images using this specific implementation of the butterfly approach are presented and discussed

An iterative hyperspectral image segmentation method using a cross analysis of spectral and spatial information

International audienceThe combined use of available spectral and spatial information for object detection, which has been promoted by the advent of high spatial resolution hyperspectral imaging devices, now seems essential for many application domains (characterization of urban areas, agriculture, etc.). The proposed approach called "butterfly" is focusing on this issue and realizes a spectral-spatial cooperation scheme to split images into spectrally homogeneous adjoining regions (segmentation). The main idea of the method is to extract spatial and spectral features simultaneously. For achieving this goal, it establishes some correspondences between the spatial and the spectral concepts, in order to run alternately in the two spaces. Thus, the notion of partition specific to the spatial space is associated with the notion of classes in the spectral space. In parallel, the concept of latent variable owing to the spectral space is associated with the notion of image plans in the spatial space. The proposed scheme is therefore to update the features specific to each space (i.e. partition, classes, latent variables and plans) by the knowledge of the features in the complementary space and this recursively. An implementation of this generic scheme using a split and merge strategy is given. Experimental results are presented for a synthetic image and two real hyperspectral images with different spatial resolution. Results on the set of real images are also compared to those obtained with conventional approaches

Hyperspectral imaging system calibration using image translations and Fourier transform

In this paper, we describe a methodology based on imaging system shifting and Fourier transform to recover the spatial distribution of the sensivity of a hyperspectral imaging system. The methodology mainly adresses a hyperspectral imaging system based on a CCD sensor for proximity image acquisition. The principle is to look several times at the same scene by moving the camera slightly (a few millimetres) at each acquisition. Thus, it is possible to separate what moves (scene) from what remains fixed (response of the system) using properties of Fourier transform. Tests on synthetic images have reinforced theorical results on contraints shifts and given good results with more than ten translations. Tests on real and in-laboratory images have shown the need for accurate determination of translation to avoid some disruptive effects (pattern multiplication). Nevertheless, the results are promising and have shown the potential of the methodology to correct images from spatial non-uniformity due to the imaging system (radiometric aberration due to the sensor and optic). We notice that such a methodology remains valid for any imaging system based on a charged-coupled device (CCD) sensor. (Résumé d'auteur

Proposition d'une stratégie de segmentation d'images hyperspectrales

International audienceHyper-Spectral Imaging (HIS)also known as chemical or spectroscopic imaging is an emerging technique that combines imaging and spectroscopy to capture both spectral and spatial information from an object. Hyperspectral images are made up of contiguous wavebands in a given spectral band. These images provide information on the chemical make-up profile of objects, thus allowing the differentiation of objects of the same colour but which possess make-up profile. Yet, whatever the application field, most of the methods devoted to HIS processing conduct data analysis without taking into account spatial information.Pixels are processed individually, as an array of spectral data without any spatial structure. Standard classification approaches are thus widely used (k-means, fuzzy-c-means hierarchical classification...). Linear modelling methods such as Partial Least Square analysis (PLS) or non linear approaches like support vector machine (SVM) are also used at different scales (remote sensing or laboratory applications). However, with the development of high resolution sensors, coupled exploitation of spectral and spatial information to process complex images, would appear to be a very relevant approach. However, few methods are proposed in the litterature. The most recent approaches can be broadly classified in two main categories. The first ones are related to a direct extension of individual pixel classification methods using just the spectral dimension (k-means, fuzzy-c-means or FCM, Support Vector Machine or SVM). Spatial dimension is integrated as an additionnal classification parameter (Markov fields with local homogeneity constrainst (Prony:2000), Support Vector Machine or SVM with spectral and spatial kernels combination {CampsValls,2006), geometrically guided fuzzy C-means (Noordam,2002)...). The second ones combine the two fields related to each dimension (spectral and spatial), namely chemometric and image analysis. Various strategies have been attempted. The first one is to rely on chemometrics methods (Principal Component Analysis or PCA, Independant Component Analysis or ICA, Curvilinear Component Analysis...) to reduce the spectral dimension and then to apply standard images processing technics on the resulting score images i.e. data projection on a subspace. Another approach is to extend the definition of basic image processing operators to this new dimensionality (morphological operators for example (Benediktsson,2005 or Palmason,2003). However, the approaches mentioned above tend to favour only one description either directly or indirectly (spectral or spatial). The purpose of this paper is to propose a hyperspectral processing approach that strikes a better balance in the treatment of both kinds of information. To achieve this, a generic scheme is proposed to associate more closely the spectral and spatial aspects symmetrically and conjunctively. This method, called butterfly, aims to perform an iterative and a cross analysis of data in the spectral and the spatial domains lead to the segmentation of the hyperspectral image. The strategy is based on two steps: 1. Extraction of a spatial structure (topology) incorporating a spectral structure, 2. Extraction of a spectral structure (latent variables) incorporating a spatial structure. These steps are processed in a successive, iterative and inter-dependent way. In this article, we will focus solely on specific notions of topology i.e. the notions of connectivity and adjacency. Thus, the first stage deals with the use of commonly used image processing tools (region segmentation algorithms) on a limited number of score images. This makes it relatively easy to process. To carry out the second step, we use chemometric tools to reveal a subspace (latent variables) enabling the characterization of heterogeneity of the obtained image partitions. However, the scheme can be applied on two different ways depending on the region segmentation strategy used i.e. top down approaches (splitting) or bottom-up approaches (merging). We have implemented this scheme by using a split and merge strategy based on the quadtree approach. Each phase is done over successive steps (iterations). At each iteration of the split phase, the data are projected on k suitable latent variables. The split of each existing region (partition) into four disjoints quadrants is tested and the one maximising the Wilks Lambda parameter is chosen. At each iteration of the merge phase, the data are projected on k' suitable latent variables and all the merging of two adjacent regions are tested. The one maximising the Wilks Lambda parameter is chosen. Lastly, we tested our approach on a simple synthetic image to show more precisely its characteristics and also on two real images at different scales (in field acquisition on crop, remote sensing image of urban zone). The results obtained on real images underline the benefit of the butterfly approach. However, futher work will be undertaken to investigate the influence of various parameters. Moreover, other topology notions and image analysis algorithm could be also investigated.Cet article présente une stratégie de segmentation d'images hyperspectrales liant de façon symétrique et conjointe les aspects spectraux et spatiaux. Pour cela, nous proposons de construire des variables latentes permettant de définir un sous-espace représentant au mieux la topologie de l'image. Dans cet article, nous limiterons cette notion de topologie à la seule appartenance aux régions. Pour ce faire, nous utilisons d'une part les notions de l'analyse discriminante (variance intra, inter) et les propriétés des algorithmes de segmentation en région liées à celles-ci. Le principe générique théorique est exposé puis décliné sous la forme d'un exemple d'implémentation optimisé utilisant un algorithme de segmentation en région type split and merge. Les résultats obtenus sur des images de synthèse et réelle sont exposés et commentés

TRPM8 as an Anti–Tumoral Target in Prostate Cancer Growth and Metastasis Dissemination

International audienceIn the fight against prostate cancer (PCa), TRPM8 is one of the most promising clinical targets. Indeed, several studies have highlighted that TRPM8 involvement is key in PCa progression because of its impact on cell proliferation, viability, and migration. However, data from the literature are somewhat contradictory regarding the precise role of TRPM8 in prostatic carcinogenesis and are mostly based on in vitro studies. The purpose of this study was to clarify the role played by TRPM8 in PCa progression. We used a prostate orthotopic xenograft mouse model to show that TRPM8 overexpression dramatically limited tumor growth and metastasis dissemination in vivo. Mechanistically, our in vitro data revealed that TRPM8 inhibited tumor growth by affecting the cell proliferation and clonogenic properties of PCa cells. Moreover, TRPM8 impacted metastatic dissemination mainly by impairing cytoskeleton dynamics and focal adhesion formation through the inhibition of the Cdc42, Rac1, ERK, and FAK pathways. Lastly, we proved the in vivo efficiency of a new tool based on lipid nanocapsules containing WS12 in limiting the TRPM8–positive cells’ dissemination at metastatic sites. Our work strongly supports the protective role of TRPM8 on PCa progression, providing new insights into the potential application of TRPM8 as a therapeutic target in PCa treatment

NALCN ‐mediated sodium influx confers metastatic prostate cancer cell invasiveness

International audienceThere is growing evidence that ion channels are critically involved in cancer cell invasiveness and metastasis. However, the molecular mechanisms of ion signaling promoting cancer behavior are poorly understood and the complexity of the underlying remodeling during metastasis remains to be explored. Here, using a variety of in vitro and in vivo techniques, we show that metastatic prostate cancer cells acquire a specific Na+ /Ca2+ signature required for persistent invasion. We identify the Na+ leak channel, NALCN, which is overexpressed in metastatic prostate cancer, as a major initiator and regulator of Ca2+ oscillations required for invadopodia formation. Indeed, NALCN-mediated Na+ influx into cancer cells maintains intracellular Ca2+ oscillations via a specific chain of ion transport proteins including plasmalemmal and mitochondrial Na+ /Ca2+ exchangers, SERCA and store-operated channels. This signaling cascade promotes activity of the NACLN-colocalized proto-oncogene Src kinase, actin remodeling and secretion of proteolytic enzymes, thus increasing cancer cell invasive potential and metastatic lesions in vivo. Overall, our findings provide new insights into an ion signaling pathway specific for metastatic cells where NALCN acts as persistent invasion controller

Blurring the boundaries between perpetrators and victims: Pied-noir memories and the harki community

This article seeks to trace the evolving relationship between the collective memories of the pied-noir community, the former settler population of French Algeria, and the harkis, those Algerians who fought for the French during the Algerian War of Independence (1954-62). Although regarded by many as at best complicit in, and at worst the perpetrators of, a system of colonial domination, the pieds-noirs view themselves as innocent casualties of a destructive and erroneous historical force, decolonization. In light of this, the article will focus on the ways in which pieds-noirs, primarily through their strong associational network, have attempted to retrospectively redeem themselves by converting their status from that of perpetrators into that of victims by grafting their collective memories onto those of a clearly identified ‘victim’ population, the harkis with whom they feel a special affinity. The reaction of the harki community to this process and the implications for the development of their own memories will also be examined