The use of the Kalman filter in the automated segmentation of EIT lung images

In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging

Vertex-Based Delaunay Triangulation Of Meshes Of Arbitrary Topological Type

Introduction Triangular meshes have become a standard way of representing objects in computer graphics and geometric modeling. Unfortunately, highly complex triangular meshes, easily generated by laser scanning systems, can be both frustrating to edit and expensive to store, transmit, and render. Multiresolution representations of meshes, developed by Lounsbery et al. [3, 4] and extended by others [2, 5], address these problems. A multiresolution representation of a mesh consists of a simple base mesh plus a series of local correction terms, wavelet coefficients, that capture the represented object's detail at increasing levels of resolution. Multiresolution mesh representations are therefore useful for applications such as compression and the progressive display and transmission of three-dimensional graphics [2]. The one noted drawback to the Lounsbery et al. multiresolution analysis is that this method can only be applied to meshes displaying

Piecewise smooth surface reconstruction

We present a general method for automatic reconstruction of accurate, concise, piecewise smooth surface models from scattered range data. The method can be used in a variety of applications such as reverse engineering - the automatic generation of CAD models from physical objects. Novel aspects of the method are its ability to model surfaces of arbitrary topological type and to recover sharp features such as creases and corners. The method has proven to be effective, as demonstrated by a number of examples using both simulated and real data. A key ingredient in the method, and a principal contribution of this paper, is the introduction of a new class of piecewise smooth surface representations based on subdivision. These surfaces have a number of properties that make them ideal for use in surface reconstruction: they are simple to implement, they can model sharp features concisely, and they can be fit to scattered range data using an unconstrained optimization procedure

Matheuristics2008 - Second International Workshop on Model based Metaheuristics June 16 - 18, 2008 University Residential Center Bertinoro (Forl\uec-Cesena), Italy

Building on the success of the first Matheuristics meeting (August, 2006), the Matheuristics 2008 workshop is proposed as a primary forum for researchers working either on exploiting mathematical programming (MP) techniques in a (meta)heuristic framework or on granting to mathematical programming approaches the cross-problem robustness and constrained-CPU-time effectiveness which characterize metaheuristics. Discriminating landmark is some form of exploitation of the mathematical formulation of the problems of interest. Metaheuristic algorithms and frameworks, such as tabu search, genetic algorithms, VNS, etc., were in fact usually proposed in years when Mixed Integer Programming (MIP) was seldom a viable option for solving real-world problem instances, or significant subproblems thereof. However, research on mathematical programming, and in particular on discrete optimization, has led to a state of the art where MIP solvers or customized MP codes can be effective even in a heuristic context, both as primary solvers or as subprocedures. Matheuristics 2008 will help defining the state of the art for the computational effectiveness and efficiency or theoretical properties of integrated metaheuristics/MIP codes (MH codes). Matheuristics 2008 will be entirely dedicated to this new research option, the conference program will consist only of plenary presentations. All accepted presentations will be published in a conference proceedings volume. Grants will be available for Ph.D. students. Topics of interest include: * Dual information and metaheuristics; * Decompositions and lower/upper bounds in MH codes; * Upper and lower bounds interacting evolutions; * Stochastic programming and heuristic search; * Metaheuristics for stochastic problems; * Model-based metaheuristics; * MIP solvers as search components (local branching, RINS, ...); * Hybridizing (meta)heuristics and exact methods; * Experimental analysis and modeling of algorithms; * Real world case histories of successful MH applications. Matheuristics 2008 is not interested in heuristics tailored to a specific problem that have no element which can be generalized to other problems - no matter how mathematically sophisticated they are - nor in metaheuristics variants which are not justified by a mathematical model

An Integrated White+Black Box Approach for Designing and Tuning Stochastic Local Search

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)4741 LNCS332-34

AcoSeeD: An ant colony optimization for finding optimal spaced seeds in biological sequence search

Abstract. Similarity search in biological sequence database is one of the most popular and important bioinformatics tasks. Spaced seeds have been increasingly used to improve the quality and sensitivity of searching, for example, in seeded alignment methods. Finding optimal spaced seeds is a NP-hard problem. In this study we introduce an application of an Ant Colony Optimization (ACO) algorithm to address this problem in a metaheuristics framework. This method, called AcoSeeD, builds optimalspacedseedsinanelegantconstructiongraphthatusestheACO standard framework with a modified pheromone update. Experimental results demonstrate that AcoSeeD brings a significant improvement of sensitivity while demanding the same computational time as other stateof-the-art methods. We also introduces an alternative way of using local search that exerts a fast approximation of the objective function in ACO.

A software framework for fast prototyping of meta-heuristics hybridization

(Special Issue for 17th IFORS Triennial Conference