Parallelized Seeded Region Growing Using CUDA

This paper presents a novel method for parallelizing the seeded region growing (SRG) algorithm using Compute Unified Device Architecture (CUDA) technology, with intention to overcome the theoretical weakness of SRG algorithm of its computation time being directly proportional to the size of a segmented region. The segmentation performance of the proposed CUDA-based SRG is compared with SRG implementations on single-core CPUs, quad-core CPUs, and shader language programming, using synthetic datasets and 20 body CT scans. Based on the experimental results, the CUDA-based SRG outperforms the other three implementations, advocating that it can substantially assist the segmentation during massive CT screening tests

Hybrid parallelization of a seeded region growing segmentation of brain images for a GPU cluster

The introduction of novel imaging technologies always carries new challenges regarding the processing of the captured images. Polarized Light Imaging (PLI) is such a new technique. It enables the mapping of single nerve fibers in postmortem human brains in unprecedented detail. Due to the very high resolution at sub-millimeter scale, an immense amount of image data has to be reconstructed three-dimensionally before it can be analyzed. Some of the steps in the reconstruction pipeline require a previous segmentation of the large images. This task of image processing creates black-and-white masks indicating the object and background pixels of the original images. It has turned out that a seeded region growing approach achieves segmentation masks of the desired quality. To be able to process the immense number of images acquired with PLI, the region growing has to be parallelized for a supercomputer. However, the choice of the seeds has to be automated in order to enable a parallel execution. A hybrid parallelization has been applied to the automated seeded region growing to exploit the architecture of a GPU cluster. The hybridity consists of an MPI parallelization and the execution of some well-chosen, data-parallel subtasks on GPUs. This approach achieves a linear speedup behavior so that the runtime can be reduced to a reasonable amount

Fast extraction of neuron morphologies from large-scale SBFSEM image stacks

Neuron morphology is frequently used to classify cell-types in the mammalian cortex. Apart from the shape of the soma and the axonal projections, morphological classification is largely defined by the dendrites of a neuron and their subcellular compartments, referred to as dendritic spines. The dimensions of a neuron’s dendritic compartment, including its spines, is also a major determinant of the passive and active electrical excitability of dendrites. Furthermore, the dimensions of dendritic branches and spines change during postnatal development and, possibly, following some types of neuronal activity patterns, changes depending on the activity of a neuron. Due to their small size, accurate quantitation of spine number and structure is difficult to achieve (Larkman, J Comp Neurol 306:332, 1991). Here we follow an analysis approach using high-resolution EM techniques. Serial block-face scanning electron microscopy (SBFSEM) enables automated imaging of large specimen volumes at high resolution. The large data sets generated by this technique make manual reconstruction of neuronal structure laborious. Here we present NeuroStruct, a reconstruction environment developed for fast and automated analysis of large SBFSEM data sets containing individual stained neurons using optimized algorithms for CPU and GPU hardware. NeuroStruct is based on 3D operators and integrates image information from image stacks of individual neurons filled with biocytin and stained with osmium tetroxide. The focus of the presented work is the reconstruction of dendritic branches with detailed representation of spines. NeuroStruct delivers both a 3D surface model of the reconstructed structures and a 1D geometrical model corresponding to the skeleton of the reconstructed structures. Both representations are a prerequisite for analysis of morphological characteristics and simulation signalling within a neuron that capture the influence of spines

Optimización en GPU de algoritmos para la mejora del realce y segmentación en imágenes hepáticas

This doctoral thesis deepens the GPU acceleration for liver enhancement and segmentation. With this motivation, detailed research is carried out here in a compendium of articles. The work developed is structured in three scientific contributions, the first one is based upon enhancement and tumor segmentation, the second one explores the vessel segmentation and the last is published on liver segmentation. These works are implemented on GPU with significant speedups with great scientific impact and relevance in this doctoral thesis The first work proposes cross-modality based contrast enhancement for tumor segmentation on GPU. To do this, it takes target and guidance images as an input and enhance the low quality target image by applying two dimensional histogram approach. Further it has been observed that the enhanced image provides more accurate tumor segmentation using GPU based dynamic seeded region growing. The second contribution is about fast parallel gradient based seeded region growing where static approach has been proposed and implemented on GPU for accurate vessel segmentation. The third contribution describes GPU acceleration of Chan-Vese model and cross-modality based contrast enhancement for liver segmentation

Layered Fields for Natural Tessellations on Surfaces

Mimicking natural tessellation patterns is a fascinating multi-disciplinary problem. Geometric methods aiming at reproducing such partitions on surface meshes are commonly based on the Voronoi model and its variants, and are often faced with challenging issues such as metric estimation, geometric, topological complications, and most critically parallelization. In this paper, we introduce an alternate model which may be of value for resolving these issues. We drop the assumption that regions need to be separated by lines. Instead, we regard region boundaries as narrow bands and we model the partition as a set of smooth functions layered over the surface. Given an initial set of seeds or regions, the partition emerges as the solution of a time dependent set of partial differential equations describing concurrently evolving fronts on the surface. Our solution does not require geodesic estimation, elaborate numerical solvers, or complicated bookkeeping data structures. The cost per time-iteration is dominated by the multiplication and addition of two sparse matrices. Extension of our approach in a Lloyd's algorithm fashion can be easily achieved and the extraction of the dual mesh can be conveniently preformed in parallel through matrix algebra. As our approach relies mainly on basic linear algebra kernels, it lends itself to efficient implementation on modern graphics hardware.Comment: Natural tessellations, surface fields, Voronoi diagrams, Lloyd's algorith

Real-Time Collision Imminent Steering Using One-Level Nonlinear Model Predictive Control

Automotive active safety features are designed to complement or intervene a human driver's actions in safety critical situations. Existing active safety features, such as adaptive cruise control and lane keep assist, are able to exploit the ever growing sensor and computing capabilities of modern automobiles. An emerging feature, collision imminent steering, is designed to perform an evasive lane change to avoid collision if the vehicle believes collision cannot be avoided by braking alone. This is a challenging maneuver, as the expected highway setting is characterized by high speeds, narrow lane restrictions, and hard safety constraints. To perform such a maneuver, the vehicle may be required to operate at the nonlinear dynamics limits, necessitating advanced control strategies to enforce safety and drivability constraints. This dissertation presents a one-level nonlinear model predictive controller formulation to perform a collision imminent steering maneuver in a highway setting at high speeds, with direct consideration of safety criteria in the highway environment and the nonlinearities characteristic of such a potentially aggressive maneuver. The controller is cognizant of highway sizing constraints, vehicle handling capability and stability limits, and time latency when calculating the control action. In simulated testing, it is shown the controller can avoid collision by conducting a lane change in roughly half the distance required to avoid collision by braking alone. In preliminary vehicle testing, it is shown the control formulation is compatible with the existing perception pipeline, and prescribed control action can safely perform a lane change at low speed. Further, the controller must be suitable for real-time implementation and compatible with expected automotive control architecture. Collision imminent steering, and more broadly collision avoidance, control is a computationally challenging problem. At highway speeds, the required time for action is on the order of hundreds of milliseconds, requiring a control formulation capable of operating at tens of Hertz. To this extent, this dissertation investigates the computational expense of such a controller, and presents a framework for designing real-time compatible nonlinear model predictive controllers. Specifically, methods for numerically simulating the predicted vehicle response and response sensitivities are compared, their cross interaction with trajectory optimization strategy are considered, and the resulting mapping to a parallel computing hardware architecture is investigated. The framework systematically evaluates the underlying numerical optimization problem for bottlenecks, from which it provides alternative solutions strategies to achieve real-time performance. As applied to the baseline collision imminent steering controller, the procedure results in an approximate three order of magnitude reduction in compute wall time, supporting real-time performance and enabling preliminary testing on automotive grade hardware.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163063/1/jbwurts_1.pd

Fast catheter segmentation and tracking based on x-ray fluoroscopic and echocardiographic modalities for catheter-based cardiac minimally invasive interventions

X-ray fluoroscopy and echocardiography imaging (ultrasound, US) are two imaging modalities that are widely used in cardiac catheterization. For these modalities, a fast, accurate and stable algorithm for the detection and tracking of catheters is required to allow clinicians to observe the catheter location in real-time. Currently X-ray fluoroscopy is routinely used as the standard modality in catheter ablation interventions. However, it lacks the ability to visualize soft tissue and uses harmful radiation. US does not have these limitations but often contains acoustic artifacts and has a small field of view. These make the detection and tracking of the catheter in US very challenging. The first contribution in this thesis is a framework which combines Kalman filter and discrete optimization for multiple catheter segmentation and tracking in X-ray images. Kalman filter is used to identify the whole catheter from a single point detected on the catheter in the first frame of a sequence of x-ray images. An energy-based formulation is developed that can be used to track the catheters in the following frames. We also propose a discrete optimization for minimizing the energy function in each frame of the X-ray image sequence. Our approach is robust to tangential motion of the catheter and combines the tubular and salient feature measurements into a single robust and efficient framework. The second contribution is an algorithm for catheter extraction in 3D ultrasound images based on (a) the registration between the X-ray and ultrasound images and (b) the segmentation of the catheter in X-ray images. The search space for the catheter extraction in the ultrasound images is constrained to lie on or close to a curved surface in the ultrasound volume. The curved surface corresponds to the back-projection of the extracted catheter from the X-ray image to the ultrasound volume. Blob-like features are detected in the US images and organized in a graphical model. The extracted catheter is modelled as the optimal path in this graphical model. Both contributions allow the use of ultrasound imaging for the improved visualization of soft tissue. However, X-ray imaging is still required for each ultrasound frame and the amount of X-ray exposure has not been reduced. The final contribution in this thesis is a system that can track the catheter in ultrasound volumes automatically without the need for X-ray imaging during the tracking. Instead X-ray imaging is only required for the system initialization and for recovery from tracking failures. This allows a significant reduction in the amount of X-ray exposure for patient and clinicians.Open Acces

Agent-based modelling and Swarm Intelligence in systems engineering

El objetivo de la tesis doctoral es evaluar la utilidad de las técnicas Modelado Basado en Agentes, algoritmos de optimización Swarm Intelligence y programación paralela sobre tarjeta gráfica en el campo de la Ingeniería de Sistemas y Automática. Se ha realizado un revisión bibliográfica y desarrollado un marco de desarrollo de la técnica de Modelado Basado en Agentes. Esta técnica se ha empleado para realizar un modelo de un reactor de fangos activados (que se engloba dentro del proceso de depuración de aguas residuales). Se ha desarrollado una notación complementaria para la descripción de modelos basados en agentes desde el punto de vista de la ingeniería de sistemas. Se ha presentado asimismo un algoritmo de optimización basado en agentes bajo la filosofía Swarm Intelligence. Se han trabajado con las técnicas de paralelización sobre tarjeta gráfica para reducir los tiempos de simulación de modelos y algoritmos. Se trata por lo tanto de un tesis de integración de varias tecnologías.Departamento de Ingeniería de Sistemas y Automátic