Vascular Complexity Evaluation Using a Skeletonization Approach and 3D LED-Based Photoacoustic Images

Vasculature analysis is a fundamental aspect in the diagnosis, treatment, outcome evaluation and follow-up of several diseases. The quantitative characterization of the vascular network can be a powerful means for earlier pathologies revealing and for their monitoring. For this reason, non-invasive and quantitative methods for the evaluation of blood vessels complexity is a very important issue. Many imaging techniques can be used for visualizing blood vessels, but many modalities are limited by high costs, the need of exogenous contrast agents, the use of ionizing radiation, a very limited acquisition depth, and/or long acquisition times. Photoacoustic imaging has recently been the focus of much research and is now emerging in clinical applications. This imaging modality combines the qualities of good contrast and the spectral specificity of optical imaging and the high penetration depth and the spatial resolution of acoustic imaging. The optical absorption properties of blood also make it an endogenous contrast agent, allowing a completely non-invasive visualization of blood vessels. Moreover, more recent LED-based photoacoustic imaging systems are more affordable, safe and portable when compared to a laser-based systems. In this chapter we will confront the issue of vessel extraction techniques and how quantitative vascular parameters can be computed on 3D LED-based photoacoustic images using an in vitro vessel phantom model

Bioinspired parallel 2D or 3D skeletonization

Algebraic Topology has been proved to be an useful tool to be used in image processing. In this case we will borrow some elements from Algebraic Topology in order to show a parallel algorithm for thinning a binary 3D image respecting its shape information. The parallelization of the thinning algorithm is based on Membrane Computing. This research area has already been proved to be useful in the development of parallel image processing algorithms. We present here the main guidelines of the algorithms along with a slight introduction about some basic required knowledge about Algebraic Topology and Membrane Computing

Advanced Water Distribution Modeling and Management

Advanced Water Distribution Modeling and Management builds on Haestad Press’ Water Distribution Modeling book. Addressing the modeling process from data collection to application, Advanced Water Distribution Modeling and Management adds extensive material from an international team of experts from both academia and consulting firms and includes topics such as: In-depth coverage of optimization techniques for model calibration, system design, and pump operations. Advanced water quality modeling topics including tank mixing, water quality solution algorithms, sampling techniques, tracer studies, tank design, and maintenance of adequate disinfectant residuals. Integration of SCADA systems with water distribution modeling for estimating model demands, initial conditions, and control settings; forecasting system operations; calibrating extended-period simulation models; streamlining water quality analysis; and estimating water loss during a main break. The essentials of transient analysis including the causes and sources of transients, as well as the potential effects of transients on water distribution systems. Application of GIS technology for skeletonization, demand allocation, and pipe break analysis; discussion of the technological issues that arise when integrating GIS and water distribution modeling; and the current state of the technology. Use of models to assess water system vulnerability and security, respond to emergencies in real-time, simulate contamination events, prioritize physical security improvements, and unravel past contamination events

Recognition and reconstruction of coherent energy with application to deep seismic reflection data

Reflections in deep seismic reflection data tend to be visible on only a limited number of traces in a common midpoint gather. To prevent stack degeneration, any noncoherent reflection energy has to be removed. In this paper, a standard classification technique in remote sensing is presented to enhance data quality. It consists of a recognition technique to detect and extract coherent energy in both common shot gathers and fi- nal stacks. This technique uses the statistics of a picked seismic phase to obtain the likelihood distribution of its presence. Multiplication of this likelihood distribution with the original data results in a “cleaned up” section. Application of the technique to data from a deep seismic reflection experiment enhanced the visibility of all reflectors considerably. Because the recognition technique cannot produce an estimate of “missing” data, it is extended with a reconstruction method. Two methods are proposed: application of semblance weighted local slant stacks after recognition, and direct recognition in the linear tau-p domain. In both cases, the power of the stacking process to increase the signal-to-noise ratio is combined with the direct selection of only specific seismic phases. The joint application of recognition and reconstruction resulted in data images which showed reflectors more clearly than application of a single technique

Writing Reusable Digital Geometry Algorithms in a Generic Image Processing Framework

Digital Geometry software should reflect the generality of the underlying mathe- matics: mapping the latter to the former requires genericity. By designing generic solutions, one can effectively reuse digital geometry data structures and algorithms. We propose an image processing framework focused on the Generic Programming paradigm in which an algorithm on the paper can be turned into a single code, written once and usable with various input types. This approach enables users to design and implement new methods at a lower cost, try cross-domain experiments and help generalize resultsComment: Workshop on Applications of Discrete Geometry and Mathematical Morphology, Istanb : France (2010