Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors

We propose a level set based variational approach that incorporates shape priors into edge-based and region-based models. The evolution of the active contour depends on local and global information. It has been implemented using an efficient narrow band technique. For each boundary pixel we calculate its dynamic according to its gray level, the neighborhood and geometric properties established by training shapes. We also propose a criterion for shape aligning based on affine transformation using an image normalization procedure. Finally, we illustrate the benefits of the our approach on the liver segmentation from CT images

Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data

Probabilistic Atlas Based Segmentation Using Affine Moment Descriptors and Graph-Cuts

We show a procedure for constructing a probabilistic atlas based on affine moment descriptors. It uses a normalization procedure over the labeled atlas. The proposed linear registration is defined by closed-form expressions involving only geometric moments. This procedure applies both to atlas construction as atlas-based segmentation. We model the likelihood term for each voxel and each label using parametric or nonparametric distributions and the prior term is determined by applying the vote-rule. The probabilistic atlas is built with the variability of our linear registration. We have two segmentation strategy: a) it applies the proposed affine registration to bring the target image into the coordinate frame of the atlas or b) the probabilistic atlas is non-rigidly aligning with the target image, where the probabilistic atlas is previously aligned to the target image with our affine registration. Finally, we adopt a graph cut - Bayesian framework for implementing the atlas-based segmentation

Approximations of Shape Metrics and Application to Shape Warping and Empirical Shape Statistics

International audienceThis chapter proposes a framework for dealing with two problems related to the analysis of shapes: the definition of the relevant set of shapes and that of defining a metric on it. Following a recent research monograph by Delfour and Zolésio [8], we consider the characteristic functions of the subsets of ℝ2 and their distance functions. The L 2 norm of the difference of characteristic functions and the L∞ and the W 1,2 norms of the difference of distance functions define interesting topologies, in particular that induced by the well-known Hausdorff distance. Because of practical considerations arising from the fact that we deal with image shapes defined on finite grids of pixels, we restrict our attention to subsets of ℝ2 of positive reach in the sense of Federer [12], with smooth boundaries of bounded curvature. For this particular set of shapes we show that the three previous topologies are equivalent. The next problem we consider is that of warping a shape onto another by infinitesimal gradient descent, minimizing the corresponding distance. Because the distance function involves an inf, it is not differentiable with respect to the shape. We propose a family of smooth approximations of the distance function which are continuous with respect to the Hausdorff topology, and hence with respect to the other two topologies. We compute the corresponding Gâteaux derivatives. They define deformation flows that can be used to warp a shape onto another by solving an initial value problem. We show several examples of this warping and prove properties of our approximations that relate to the existence of local minima. We then use this tool to produce computational de.nitions of the empirical mean and covariance of a set of shape examples. They yield an analog of the notion of principal modes of variation. We illustrate them on a variety of examples

An applied methodology for stakeholder identification in transdisciplinary research

In this paper we present a novel methodology for identifying stakeholders for the purpose of engaging with them in transdisciplinary, sustainability research projects. In transdisciplinary research, it is important to identify a range of stakeholders prior to the problem-focussed stages of research. Early engagement with diverse stakeholders creates space for them to influence the research process, including problem definition, from the start. However, current stakeholder analysis approaches ignore this initial identification process, or position it within the subsequent content-focussed stages of research. Our methodology was designed as part of a research project into a range of soil threats in seventeen case study locations throughout Europe. Our methodology was designed to be systematic across all sites. It is based on a snowball sampling approach that can be implemented by researchers with no prior experience of stakeholder research, and without requiring significant financial or time resources. It therefore fosters transdisciplinarity by empowering physical scientists to identify stakeholders and understand their roles. We describe the design process and outcomes, and consider their applicability to other research projects. Our methodology therefore consists of a two-phase process of design and implementation of an identification questionnaire. By explicitly including a design phase into the process, it is possible to tailor our methodology to other research projects

A spindle cell carcinoma presenting with osseous metaplasia in the gingiva: a case report with immunohistochemical analysis

<p>Abstract</p> <p>Background</p> <p>Spindle cell carcinoma (SpCC) is a rare, high malignant variant of squamous cell carcinoma (SCC), which shows biphasic proliferation of conventional SCC component and malignant spindle shape cells with sarcomatous appearance.</p> <p>Methods</p> <p>A case of Spindle cell carcinoma with bone-like calcified materials, occurring at the mandibular molar region of 71-years-old Japanese male patient was presented with gross finding, histological findings and MRI image. To identify the characteristics of the bone-like materials, immunohistochemistry were performed.</p> <p>Results</p> <p>Histologically, the cancer cells were composed of spindle cells and epithelial cells which form nests with prominent keratinization. Histological findings showed typical histology of the SpCC, however, as an uncommon finding, spatters of calcified, bone-like materials were observed in between the cancer cells. Immunohistochemistry revealed that cancer cells were positive for cytokeratins and vimentin to a varying degree and negative for Desmin, S-100, Osteopontin, BMP-2 or BMP-4. These findings implied that the calcified materials were formed by metaplasia of the stromal cells.</p> <p>Discussion</p> <p>Bone-like materials formation by osseous and/or cartilaginous metaplasia of the stroma in the carcinoma has been reported. However, the detailed mechanism of these metaplasia and affection on the clinical feature, prognosis and therapies are not well established. In summary, we presented an unique case of SpCC, which has not been described in the literature.</p

Governance Conditions for Improving Quality Drinking Water Resources: the Need for Enhancing Connectivity

Realising the water quality objectives of the European Water Framework Directive have appeared to stagnate over the last decade all across Europe because of their highly complex nature. In the literature, interactive governance approaches tend to be regarded as the best way of dealing with complex water issues, but so far little empirical evidence has been reported on this perspective in regard to water quality issues. In this paper we have analysed how conditions of governance contribute to the realisation of water quality objectives at different types of drinking water resources in the Netherlands. The analysis demonstrates the importance of addressing different hydrological scales, institutional levels and sectors and thus enhance connectivity in order to improve water quality. The two other important conditions of governance approaches for water quality improvement which were identified are the use of joint fact-finding to gain a shared perception of risks, and the use of explicit decision-making and close monitoring of outcomes (re. water quality improvement), both of which contribute to this enhanced connectivity

A Bayesian Approach to Sparse Model Selection in Statistical Shape Models

Groupwise registration of point sets is the fundamental step in creating statistical shape models (SSMs). When the number of points on the sets varies across the population, each point set is often regarded as a spatially transformed Gaussian mixture model (GMM) sample, and the registration problem is formulated as the estimation of the underlying GMM from the training samples. Thus, each Gaussian in the mixture specifies a landmark (or model point), which is probabilistically corresponded to a training point. The Gaussian components, transformations, and probabilistic matches are often computed by an expectation-maximization (EM) algorithm. To avoid over- and under-fitting errors, the SSM should be optimized by tuning the required number of components. In this paper, rather than manually setting the number of components before training, we start from a maximal model and prune out the negligible points during the registration by a sparsity criterion. We show that by searching over the continuous space for optimal sparsity level, we can reduce the fitting errors (generalization and specificities), and thereby help the search process for a discrete number of model points. We propose an EM framework, adopting a symmetric Dirichlet distribution as a prior, to enforce sparsity on the mixture weights of Gaussians. The negligible model points are pruned by a quadratic programming technique during EM iterations. The proposed EM framework also iteratively updates the estimates of the rigid registration parameters of the point sets to the mean model. Next, we apply the principal component analysis to the registered and equal-length training point sets and construct the SSMs. This method is evaluated by learning of sparse SSMs from 15 manually segmented caudate nuclei, 24 hippocampal, and 20 prostate data sets. The generalization, specificity, and compactness of the proposed model favorably compare to a traditional EM based model

Identifying Where REDD+ Financially Out Competes Oil Palm in Floodplain Landscapes Using a Fine-Scale Approach

Reducing Emissions from Deforestation and forest Degradation (REDD+) aims to avoid forest conversion to alternative land-uses through financial incentives. Oil-palm has high opportunity costs, which according to current literature questions the financial competitiveness of REDD+ in tropical lowlands. To understand this more, we undertook regional finescale and coarse-scale analyses (through carbon mapping and economic modelling) to assess the financial viability of REDD+ in safeguarding unprotected forest (30,173 ha) in the Lower Kinabatangan floodplain in Malaysian Borneo. Results estimate 4.7 million metric tons of carbon (MgC) in unprotected forest, with 64% allocated for oil-palm cultivations. Through fine-scale mapping and carbon accounting, we demonstrated that REDD+ can outcompete oil-palm in regions with low suitability, with low carbon prices and low carbon stock. In areas with medium oil-palm suitability, REDD+ could outcompete oil palm in areas with: very high carbon and lower carbon price; medium carbon price and average carbon stock; or, low carbon stock and high carbon price. Areas with high oil palm suitability, REDD + could only outcompete with higher carbon price and higher carbon stock. In the coarse-scale model, oil-palm outcompeted REDD+ in all cases. For the fine-scale models at the landscape level, low carbon offset prices (US $3 MgCO2e) would enable REDD+ to outcompete oil-palm in 55$27 million to secure these areas for 25 years. Higher carbon offset price (US $30 MgCO2e) would increase the competitiveness of REDD+ within the landscape but would still only capture between 69$380–416 million in carbon financing. REDD+ has been identified as a strategy to mitigate climate change by many countries (including Malaysia). Although REDD+ in certain scenarios cannot outcompete oil palm, this research contributes to the global REDD+ debate by: highlighting REDD+ competitiveness in tropical floodplain landscapes; and, providing a robust approach for identifying and targeting limited REDD+ funds