32209678
A Novel Coarse-to-Fine Adaptation Segmentation Approach for Cellular Image Analysis
Cellular image content analysis is one of the most important aspects of the cellular research and often requires collecting a great amount of statistical information and phenomena. Automated segmentation of time-lapse images gradually becomes the key problem in cellular image analysis. To address fuzzy, irregular, and ruffling cell boundaries in time-lapse cellular images, this paper introduces a hierarchical coarse-to-fine approach which is composed of iteration-dependent adaptation procedures with high-level interpretation: initial segmentation, adaptive processing, and refined segmentation. The iteration-dependent adaptation lies in that the adaptive processing and the refined segmentation be deliberately designed without a fixed order and a uniform associated iteration number, to connect coarse segmentation and refined segmentation for locally progressive approximation. The initial segmentation could avoid over-segmentation from watershed transform and converge to some features using a priori information. Experimental results on cellular images with spurious branches, arbitrary gaps, low contrast boundaries and low signal-to-noise ratio, show that the proposed approach provides a close matching to the manual cognition and overcomes several common drawbacks from other existing methods applied on cell migration. The procedure configuration of the proposed approach has a certain potential to serve as a biomedical image content analysis tool.
7470395
A Simulation-Based 3D Axon Axis Extraction in Confocal Fluorescence Microscopy Images
Neuron axon analysis is a fundamental mean to investigate nervous system in neurobiology and often requires collecting a great amount of statistical information. Automated extraction of axons axis in 3D microscopic images posts a key problem in the field of neuron axon analysis. To address tortuous axons with several touchings in 3D volumes, a novel simulation based approach with regard to volume modeling and scenario mapping is presented. Given the simulation model, the properties of dataset and the nature of axon (e.g., continuity) are converted to physics laws including the principle of minimum potential energy, law of buoyancy, and law of inertia. Experimental results on axon volumes show the proposed algorithm can effectively handle axon touching problem and actually extract multiple axon axes.
225470
A 3D Self-Adjust Region Growing Method for Axon Extraction
Neuron axon analysis is an important means to investigate disease mechanisms and signaling pathways in neurobiology and often requires collecting a great amount of statistical information and phenomena. Automated extraction of axons in 3D microscopic images posts a key problem in the field of neuron axon analysis. To address tortuous axons in 3D volumes, a self-adjust region growing approach referring to surface modeling and self-adjustment which takes advantage of the nature of axon (e.g., continuity), is presented. Experimental results on axon volumes show that the proposed scheme provides a reliable solution to axon retrieving and overcomes several common drawbacks from other existing methods.
2401406
Graph Theory Application in Cell Nuleus Segmentation, Tracking and Identification
A novel cell nucleus tracking approach is raised in this paper, developed from applied graph theory that simultaneously handles major challenges of cells touching, splitting, disappearing and emerging. The key point of our work lies in the graphs with robust structures that constructed from large cells groups. Then the problem of nucleus tracking is simplified to that of vertex matching between graphs generated from successive frames. Single cells are modeled as vertices in the graph and edges are built between neighboring ones, through which cells in large quantities are connected altogether. The algorithm works through a process of graph saturation following vertices with maximal entering belief-propagation flows. Feasibility of our approach is validated by so-called 'Crowds Model' that explains motional features of multi-cells groups. In processing CT image sources with high cell intensities as well as complex variations in structures, robustness of our approach is confirmed in that both successful tracking rates and segmentation rates are kept above 96%.
13932512
Joint Transmit Rate, Power and Antenna Allocation for MIMO Systems with Multimedia Traffic
This paper proposes an adaptive transmission scheme for MIMO systems to provide different bit error rates and transmission rates for multimedia traffic. The adaptive transmission scheme allocates antennas, rate and power jointly according to the feedback information to satisfy the diverse QoS requirements of the multimedia traffic. Furthermore, an efficient search algorithm with low complexity is proposed for practical implementation. Simulation results show that the proposed scheme improves the spectral efficiency while guaranteeing the QoS requirements of multimedia traffic. Moreover, the proposed search algorithm achieves close optimal performance with great complexity reduction.
15756929
Adaptive Orthonormal Random Beamforming and Multi-Beam Selection for Cellular Systems
Channel state information (CSI) at transmitter plays an important role for multiuser MIMO broadcast channels, but full CSI at transmitter is not available for many practical systems. Previous work has proposed orthonormal random beamforming (ORBF) [16] for MIMO broadcast channels with partial channel state information (CSI) feedback, and shown that ORBF achieves the optimal sum-rate capacity for a large number of users. However, for cellular systems with moderate number of users, i.e., no more than 64, ORBF only achieves slight performance gain. Therefore, we analyze the performance of ORBF with moderate number of users and total transmit power constraint and show that ORBF scheme is more efficient under low SNR. Then we propose an adaptive ORBF scheme that selects the number of random beams for simultaneous transmission according to the average signal-to-noise ratio (SNR). Moreover, a multi-beam selection (MBS) scheme that jointly selects the number and the subset of the multiple beams is proposed to further improve the system performance for low SNR cases. The simulation results show that the proposed schemes achieve significant performance improvement when the number of users is moderate.
35977333
Multi-Media Retrieval with Semantic Web: A Case Study in Airport Security Inspection Applications
Multi-media retrieval based on content meaning is an important issue. It has significant applications in the tracing of terrorists based on airport security inspection information. In this paper, we review the defects of traditional image retrieval technology. A semantic Web based image retrieval method is proposed, which is deployed into an airport security inspection information retrieval system. We present the techniques of domain ontology construction and semantic-Web annotation for the media. An image retrieval technique based on semantic annotation is suggested, and the design of a semantic information retrieval prototype system for airport security inspection application is described.
12863588
Studies on some perfectly matched layers for one-dimensional time-dependent systems
We will analyze some perfectly matched layers (PMLs) for the one-dimensional time-dependent Maxwell system, acoustic equations and hyperbolic systems in unbounded domains. The exponential decays and convergence of the PML solutions are studied. Some finite difference schemes are proposed for the PML equations and their stability and convergence are established.
18105952
Adapting Prime Number Labeling Scheme for Directed Acyclic Graphs
Directed Acyclic Graph(DAG) could be used for modeling subsumption hierarchies. Several labeling schemes have been proposed or tailored for indexing DAG in order to efficiently explore relationships in such hierarchy. However few of them can satisfy all the requirements in response time, space, and effect of updates simultaneously. In this paper, the prime number labeling scheme is extended for DAG. The scheme invests intrinsic mapping between integer divisibility and subsumption hierarchy, which simplifies the transitive closure computations and diminishes storage redundancy, as well as inherits the dynamic labeling ability from original scheme. Performance is further improved by introducing some optimization techniques. Our extensive experimental results show that prime number labeling scheme for DAG outperforms interval-based and prefix-based labeling schemes in most cases.