A variational joint segmentation and registration framework for multimodal images

Image segmentation and registration are closely related image processing techniques and often required as simultaneous tasks. In this work, we introduce an optimization-based approach to a joint registration and segmentation model for multimodal images deformation. The model combines an active contour variational term with mutual information (MI) smoothing fitting term and solves in this way the difficulties of simultaneously performed segmentation and registration models for multimodal images. This combination takes into account the image structure boundaries and the movement of the objects, leading in this way to a robust dynamic scheme that links the object boundaries information that changes over time. Comparison of our model with state of art shows that our method leads to more consistent registrations and accurate results

La foret en Republique Democratique du Congo post-conflict: analyse d'un agenda prioritaire

Forests are ubiquitous in the Democratic Republic of Congo; they touch the cultural and economic life of most of the population and have enormous global environmental significance. After years of conflicts and mismanagement, reconstruction is key to improving living conditions and consolidating peace. At the same time, better roads and trade bring risks—threatening forests and biodiversity by facilitating logging, land conversion, and the seizure of forest rights by vested interests. Anticipating these threats, in 2002, the transitional government started a Priority Reform Agenda. This report analyses the soundness of this Agenda, the progress achieved to date, and the priorities for the future. It emphasises the nature of forests as a public good; and the importance of the rule of law, transparency and public participation in managing natural resources. It highlights the multiplicity of claims on forests; calls for multipurpose participatory land-use planning; and emphasises the need to secure traditional user rights. Beyond the risks, the return of peace to the DRC also offers a unique opportunity to take a fresh look at the second-largest rainforest in the world, and to implement innovative strategies that give priority to the environment and to local people

Forests in post-conflict Democratic Republic of Congo: analysis of a priority agenda

Forests are ubiquitous in the Democratic Republic of Congo; they touch the cultural and economic life of most of the population and have enormous global environmental significance. After years of conflicts and mismanagement, reconstruction is key to improving living conditions and consolidating peace. At the same time, better roads and trade bring risks—threatening forests and biodiversity by facilitating logging, land conversion, and the seizure of forest rights by vested interests. Anticipating these threats, in 2002, the transitional government started a Priority Reform Agenda. This report analyses the soundness of this Agenda, the progress achieved to date, and the priorities for the future. It emphasises the nature of forests as a public good; and the importance of the rule of law, transparency and public participation in managing natural resources. It highlights the multiplicity of claims on forests; calls for multipurpose participatory land-use planning; and emphasises the need to secure traditional user rights. Beyond the risks, the return of peace to the DRC also offers a unique opportunity to take a fresh look at the second-largest rainforest in the world, and to implement innovative strategies that give priority to the environment and to local people

A variational model dedicated to joint segmentation, registration, and atlas generation for shape analysis

In medical image analysis, constructing an atlas, i.e., a mean representative of an ensemble of images, is a critical task for practitioners to estimate variability of shapes inside a population, and to characterize and understand how structural shape changes have an impact on health. This involves identifying significant shape constituents of a set of images, a process called segmentation, and mapping this group of images to an unknown mean image, a task called registration, making a statistical analysis of the image population possible. To achieve this goal, we propose treating these operations jointly to leverage their positive mutual influence, in a hyperelasticity setting, by viewing the shapes to be matched as Ogden materials. The approach is complemented by novel hard constraints on the L\infty norm of both the Jacobian and its inverse, ensuring that the deformation is a bi-Lipschitz homeomorphism. Segmentation is based on the Potts model, which allows for a partition into more than two regions, i.e., more than one shape. The connection to the registration problem is ensured by the dissimilarity measure that aims to align the segmented shapes. A representation of the deformation field in a linear space equipped with a scalar product is then computed in order to perform a geometry-driven Principal Component Analysis (PCA) and to extract the main modes of variations inside the image population. Theoretical results emphasizing the mathematical soundness of the model are provided, among which are existence of minimizers, analysis of a numerical method, asymptotic results, and a PCA analysis, as well as numerical simulations demonstrating the ability of the model to produce an atlas exhibiting sharp edges, high contrast, and a consistent shape