Nowadays, lung cancer is the deadliest form of cancer. Lung nodule inspection is a crucial task for effective diagnosis, prevention, and successful treatment. In SOTA, raw CT images are often directly inspected and traditional radiography remains underdeveloped for 3D representations because of its inherent 2D nature. For facilitating lung nodule analysis in clinical trainings and evaluations, lung nodule 3D visualization approaches are then proposed from CT scan or Chest X-rays (CXR), respectively. Notably, this article proposes an attention-guided approach to visualize lung nodules in three dimensions from dual chest radiographs. Experimental results show generated 3D lung nodule representations for both considered imaging data modalities as well as their visualization through a developed virtual reality environment. The source code for our approaches will be publicly available at https://github.com/Adn-an/3D-CXR-Nodule-Vis.

We describe a method for modeling helices from planar curves. Given a polygonal curve in the ( x , y ) plane, the method computes a helix such that its orthogonal projection onto the ( x , y ) plane fits the polygonal curve . The helix curve is first sampled and the transformation matrix that best aligns points of the sampled helix to those of the polygonal curve is calculated. This transformation matrix is then used to estimate the parameters of the helix whose projection fits the polygonal curve.

The problem of reconstructing the shape of dot patterns (sampled planar connected regions) is extensively studied in the literature. Up till now, the existing works do not provide guarantee for the correctness of the obtained solution, usually the results was validated empirically according to human perception. In this article, we present a new algorithm that guarantees reconstruction of the shape for a set of points satisfying some density conditions. Many experimental results show that the algorithm usually gives an adequate reconstruction for non-uniformly and weakly-sampled patterns. An advantage of the algorithm is its simplicity. Once the Delaunay triangulation of the input data is computed, simple rules are applied to the Delaunay edges in order to select those belonging to the reconstruction graph.

Geographic greedy forwarding (GF) technique has been widely used by many algorithms for routing in sensor networks because of its high efficiency resulting from its local and memoryless nature. Hence, it ensures scalability which is a fundamental requirement for protocol applicability to large-scale sensor networks with limited resources. Nevertheless, GF suffers from a serious drawback when packets, based on geographic distance, cannot be delivered; i.e., the so-called “local minimum phenomenon”. This problem has been tackled in previous research works to guarantee packet delivery by routing around the boundaries of the hole, but at an excessive consumption of control overheads. In this paper, we propose a novel approach that exploits GF technique and guarantees at the same time packet delivery (handles the local minimum situations). Our approach is of a local nature that does not retain memories and performs better than the state-of-the-art approaches in terms of its ability to guarantee packet delivery and to derive efficient routing paths. We provide, in this paper, proof of its correctness (packet delivery guaranty) while showing, through simulations, its performance effectiveness in terms of reducing path lengths, average end-to-end delays, and overall energy consumption.

Although Wireless Sensor Networks (WSNs) are capable of generating a huge amount of data, the ultimate objective of the underlying applications/end-users is to derive an estimate of a parameter or function of interest through queries sent to nodes containing raw data. The way these queries are handled by the network has a deep impact on its overall performances ( e.g., required communications, consumed energy, etc.). Among the numerous centralized and distributed approaches addressing this issue, serial ones have shown an interesting improvements in terms of reducing communication needed for each query and hence reducing required energy. Nevertheless, serial approaches suffer two main drawbacks: (a) they require to construct a path passing through all nodes of the network (which is known to be a NP-Complete problem) and (b) they experience poor scalability. In this paper, we investigate these issues by proposing a novel localized serial approach, called Peeling Algorithm (PA). The proposed approach, because of its localized nature (i.e., no extra-information is needed rather than what it is already available at each node), has shown better support for scalability while reducing significantly needed communications to accomplish a query. The extensive simulation evaluations we made have confirmed the effectiveness of our proposed approach in comparison to other serial approaches. We also provide in this paper formal proofs of its correctness i.e., our distributed approach terminates (free of looping) and visits all connected nodes in the network.

Besides of its high efficiency, in particular its scalability, geographic greedy routing is of a memoryless nature (i.e., nodes do not maintain any routing tables) which makes it well suitable for large-scale sensor networks with limited resources. Nevertheless, the occurrence of local minimum phenomenon, especially in networks with many holes, limits considerably the applicability of such routing protocol. The local minimum situations occur when the packet could not be delivered by the current node based only on distance to the destination. This problem has been tackled in previous research works to guarantee packets delivery by routing around the boundaries of the hole but at an excessive consumption of control overheads. In this paper, we propose and study a novel approach that is of memoryless nature and performs better that the state-of-art approaches in terms of guarantying packet delivery and deriving efficient routing paths. We provide in this paper proofs of its correctness (guarantying packet delivery) whereas showing, through simulations, its performance effectiveness in terms of reducing routing paths.

When a local optimal solution is reached with classical Particle Swarm Optimization (PSO), all particles in the swarm gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present in this paper a novel variant of PSO algorithm, called MPSOM, that uses Metropolis equation to update local best solutions (lbest) of each particle and uses mutation operator to escape from local optima. The proposed MPSOM algorithm is validated on seven standard benchmark functions and used to solve the problem of reducing memory energy consumption in embedded systems (Scratch-Pad Memories SPMs). The numerical results show that our approach outperforms several recently published algorithms.

The three-dimensional [Formula: see text]-shape is based on a mathematical formalism which determines exact relationships between points and shapes. It reconstructs surface and volume and detects 3D dot patterns for a given point cloud. [Formula: see text]-shape of a set of points is a sub-complex of Delaunay triangulation of this set. It generates a family of shapes according to the selected [Formula: see text] (a set of points). A method to compute the positions of the points of [Formula: see text] is proposed. These points are selected from the vertices of Voronoi diagram by analyzing the form of the polytopes; their elongation. This method allows the [Formula: see text]-shape to reflect different levels of detail in different parts of space. An efficient algorithm computing the three-dimensional [Formula: see text]-shape is presented, the [Formula: see text]-shape of a set of points is derived from the Delaunay triangulation of the same set. The speed of the algorithm is determined by the speed of the algorithm computing the Delaunay triangulation.

This paper presents a new binary shape sampling and coding method. Within the framework of the MPEG-4 standard, the binary shapes in video object plans have to be coded with a near to lossless approach in order to obtain a perfect spatial and temporal localization. For this purpose, a new sampling method based on the object skeleton is developed. The "r-sampling" is defined function of the local feature size (LFS) which is the distance between a shape point and the closest point on the medial axis of this shape. A weighting convex function /spl phi/ depending on the local curvature is also introduced to increase the local adaptiveness of the sampling ("the r&c-sampling"). This new approach is tested on contours presenting high level details. The first results obtained are satisfactory in terms of both the reduction of samples and the reconstruction error.

We present an extension of the concept /spl Ascr/-shape to weighted points, where each point's weight is the radius of a disc centered at this point. Also, we show that the resultant concept, named the weighted /spl Ascr/-shape, is a graph extracted from the regular triangulation. This new concept unifies the two descriptors, named /spl alpha/-shape and space filling graph. This unification is defined by the fact that the families of the last two descriptors are contained in the weighted /spl Ascr/-shape family; this allows the /spl Ascr/-shape to escape the limits of these two descriptors.

Article A-shapes of a finite point set Share on Author: Mahmoud Melkemi L.I.G.I.M. Université Claude Bernard Lyonl. 43 boulevard du 11 Novembre 1918, Bat. 710, 69622 Villeurbanne L.I.G.I.M. Université Claude Bernard Lyonl. 43 boulevard du 11 Novembre 1918, Bat. 710, 69622 VilleurbanneView Profile Authors Info & Claims SCG '97: Proceedings of the thirteenth annual symposium on Computational geometryAugust 1997 Pages 367–369https://doi.org/10.1145/262839.263005Online:01 August 1997Publication History 18citation359DownloadsMetricsTotal Citations18Total Downloads359Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We propose some aspects of range image processing (filtering, segmentation) based on multiresolution analysis, where we use discrete coiflet transforms. The interesting property of this kind of wavelets is the interpolating characteristic of their associated scaling functions. This characteristic is due to the fact that both coiflets and their scaling functions moments vanish. We give some definitions of range images, how they are acquired and treated. Then we present a new filtering method for such images, where we outline the importance of the interpolation property of the coiflet scaling function. We also describe an accelerating method for the range image segmentation process which uses region-growing techniques based on functions approximation and the local neighborhood property. Experimental results are given as examples.

Recursive filtering appears as an efficient tool to implement a robust multiscale edge detection schemes. In this context, and based on Canny's (1983) design, Deriche (1987) propose a set of optimum filters.>

This paper presents an efficient algorithm to find the shape of a set of digital points. We compute the true digital Voronoi tessellation in O(m/sup 2/) complexity. The algorithm needs an m/spl times/m image and the coordinates of the n initial points. The shape is then computed, in O(n) expected time, as a subgraph of the Delaunay triangulation.>

Cooperative methods provide comfortable solutions to segmentation problems. Different works have been engaged in this way in order to combine edge and region based methods. The authors propose a split and merge approach to decompose the image into homogeneous Voronoi polygons. The cooperative aspect comes from the fact that some contours of Voronoi polygons are supported by frontiers issued from an edge detector. From a geometrical point of view, the originality of this work is in the use of punctual Voronoi diagrams as an approximation of generalized Voronoi diagrams based on polygonal seed.>