Delphine Nain

Vessel Segmentation Using a Shape Driven Flow

We present a segmentation method for vessels using an implicit deformable model with a soft shape prior. Blood vessels are challenging structures to segment due to their branching and thinning geometry as well as the decrease in image contrast from the root of the vessel to its thin branches. Using image intensity alone to deform a model for the task of segmentation often results in leakages at areas where the image information is ambiguous. To address this problem, we combine image statistics and shape information to derive a region-based active contour that segments tubular structures and penalizes leakages. We present results on synthetic and real 2D and 3D datasets.

Expressive autonomous cinematography for interactive virtual environments

We have created an automatic cinematography system for interactive virtual environments. This system controls a virtual camera and fights in a three-dimensional virtual world inhabited by a group of autonomous and user-controlled characters. By dynamically changing the camera and the fights, our system facilitates the interaction of human participants with this world and displays the emotional content of the digital scene. Building on the tradition of cinema, modern video games and autonomous behavior systems, we have constructed this cinematography system with an ethologically-inspired structure of sensors, emotions, motivations, and action-selection mechanisms. Our system breaks shots into elements, such as which actors the camera should focus on or the angle it should use to watch them. Hierarchically arranged cross-exclusion groups mediate between the various options, arriving at the best shot at each moment in time. Our cinematography system uses the same approach that we use for our virtual actors. This eases the crossover of information between them, and ultimately leads to a richer and more unified installation. As digital visualizations grow more complex, cinematography must keep pace with the new breeds of characters and scenarios. A behavior-based autonomous cinematography system is an effective tool in the creation of interesting virtual worlds. Our work takes first steps toward a future of interactive, emotional cinematography.

Multiscale 3-D Shape Representation and Segmentation Using Spherical Wavelets

This paper presents a novel multiscale shape representation and segmentation algorithm based on the spherical wavelet transform. This work is motivated by the need to compactly and accurately encode variations at multiple scales in the shape representation in order to drive the segmentation and shape analysis of deep brain structures, such as the caudate nucleus or the hippocampus. Our proposed shape representation can be optimized to compactly encode shape variations in a population at the needed scale and spatial locations, enabling the construction of more descriptive, nonglobal, nonuniform shape probability priors to be included in the segmentation and shape analysis framework. In particular, this representation addresses the shortcomings of techniques that learn a global shape prior at a single scale of analysis and cannot represent fine, local variations in a population of shapes in the presence of a limited dataset. Specifically, our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We further refine the shape representation by separating into groups wavelet coefficients that describe independent global and/or local biological variations in the population, using spectral graph partitioning. We then learn a prior probability distribution induced over each group to explicitly encode these variations at different scales and spatial locations. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to two different brain structures, the caudate nucleus and the hippocampus, of interest in the study of schizophrenia. We show: 1) a reconstruction task of a test set to validate the expressiveness of our multiscale prior and 2) a segmentation task. In the reconstruction task, our results show that for a given training set size, our algorithm significantly improves the approximation of shapes in a testing set over the Point Distribution Model, which tends to oversmooth data. In the segmentation task, our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm, by capturing finer shape details

Hybrid geodesic region-based curve evolutions for image segmentation

In this paper we present a gradient descent flow based on a novel energy functional that is capable of producing robust and accurate segmentations of medical images. This flow is a hybridization of local geodesic active contours and more global region-based active contours. The combination of these two methods allows curves deforming under this energy to find only significant local minima and delineate object borders despite noise, poor edge information, and heterogeneous intensity profiles. To accomplish this, we construct a cost function that is evaluated along the evolving curve. In this cost, the value at each point on the curve is based on the analysis of interior and exterior means in a local neighborhood around that point. We also demonstrate a novel mathematical derivation used to implement this and other similar flows. Results for this algorithm are compared to standard techniques using medical and synthetic images to demonstrate the proposed methods robustness and accuracy as compared to both edge-based and region-based alone.

Integrated routing and storage for messaging applications in mobile ad hoc networks

This paper is motivated by the observation that traditional ad hoc routing protocols are not an adequate solution for messaging applications (e.g., e-mail) in mobile ad hoc networks. Routing in ad hoc mobile networks is challenging mainly because of node mobility – the more rapid the rate of movement, the greater the fraction of bad routes and undelivered messages. For applications that can tolerate delays beyond conventional forwarding delays, we advocate a relay-based approach to be used in conjunction with traditional ad hoc routing protocols. This approach takes advantage of node mobility to disseminate messages to mobile nodes. The result is the Mobile Relay Protocol (MRP), which integrates message routing and storage in the network; the basic idea is that if a route to a destination is unavailable, a node performs a controlled local broadcast (a relay) to its immediate neighbors. In a network with sufficient mobility – precisely the situation when conventional routes are likely to be non-existent or broken – it is quite likely that one of the relay nodes to which the packet has been relayed will encounter a node that has a valid, short (conventional) route to the eventual destination, thereby increasing the likelihood that the message will be successfully delivered. Our simulation results under a variety of node movement models demonstrate that this idea can work well for applications that prefer reliability over latency.

Intra-patient Prone to Supine Colon Registration for Synchronized Virtual Colonoscopy

In this paper, we present an automated method for colon registration. The method uses dynamic programming to align data defined on colon center-line paths, as extracted from the prone and supine scans. This data may include information such as path length and curvature as well as descriptors of the shape and size of the colon near the path. We show how our colon registration technique can be used to produce synchronized fly-through or slice views.

Multiscale 3D shape analysis using spherical wavelets

Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data.

STATISTICAL SHAPE ANALYSIS OF BRAIN STRUCTURES USING SPHERICAL WAVELETS

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. As an application, we analyze two brain structures, the caudate nucleus and the hippocampus, and compare the results obtained to shape analysis using a sampled point representation. Our results show that the SWC representation indicates new areas of significance preserved under the FDR correction for both the left caudate nucleus and left hippocampus. Additionally, the spherical wavelet representation provides a natural way to interpret the significance results in terms of scale in addition to knowing the spatial location of the regions

Shape-Driven 3d segmentation using spherical wavelets

This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details.

A Laplace equation approach for shape comparison

In this paper we propose a principled approach for shape comparison. Given two surfaces, one to one correspondences are determined using the Laplace equation. The distance between corresponding points is then used to define both global and local dissimilarity statistics between the surfaces. This technique provides a powerful method to compare shapes both locally and globally for the purpose of segmentation, registration or shape analysis. For improved accuracy, we propose a Boundary Element Method. Our approach is applicable to datasets of any dimension and offers subpixel resolution. We illustrate the usefulness of the technique for validation of segmentation, by defining global dissimilarity statistics and visualizing errors locally on color-coded surfaces. We also show how our technique can be applied to multiple shapes comparison.