3186271
Three-Dimensional Volume Reconstruction Based on Trajectory Fusion from Confocal Laser Scanning Microscope Images
In this paper, we address the problem of 3D volume reconstruction from depth adjacent subvolumes (i.e., sets of image frames) acquired using a confocal laser scanning microscope (CLSM). Our goal is to align sub-volumes by estimating an optimal global image transformation which preserves morphological smoothness of medical structures (called features, e.g., blood vessels) inside of a reconstructed 3D volume. We approached the problem by learning morphological characteristics of structures inside of each sub-volume, i.e. centroid trajectories of features. Next, adjacent sub-volumes are aligned by fusing the morphological characteristics of structures using extrapolation or model fitting. Finally, a global sub-volume to subvolume transformation is computed based on the entire set of fused structures. The trajectory-based 3D volume reconstruction method described here is evaluated with a pair of consecutive physical sections using two evaluation metrics for morphological continu
852427
Multisensor raster and vector data fusion based on uncertainty modeling
We propose a new methodology for fusing temporally changing multisensor raster and vector data by developing a spatially and temporally varying uncertainty model of acquired and transformed multisensor measurements. The proposed uncertainty model includes errors due to (1) each sensor by itself, e.g., sensor noise; (2) transformations of measured values to obtain comparable physical entities for data fusion and/or to calibrate sensor measurements; (3) vector data spatial interpolation that is needed to match different spatial resolutions of multisensor data; and (4) temporal interpolation that has to take place if multisensor acquisitions are not accurately synchronized. The proposed methodology was tested using simulated data with varying (a) amount of sensor noise, (b) spatial offset of point sensors generating vector data, and (c) model complexity of the underlying physical phenomenon. We demonstrated the multisensor fusion approach with a data set from a structural health monitoring application domain.
16102435
Spatial Intensity Correction of Fluorescent Confocal Laser Scanning Microscope Images
Fluorescent confocal laser scanning microscope (CLSM) imaging has become popular in medical domain for the purpose of 3D information extraction. 3D information is extracted either by visual inspection or by automated techniques. Nonetheless, 3D information extraction from CLSM suffers from significant lateral intensity heterogeneity. We propose a novel lateral intensity heterogeneity correction technique to improve accurate image analysis, e.g., quantitative analysis, segmentation, or visualization. The proposed technique is novel in terms of its design (spatially adaptive mean-weight filtering) and application (CLSM), as well as its properties and full automation. The key properties of the intensity correction techniques include adjustment of intensity heterogeneity, preservation of fine structural details, and enhancement of image contrast. The full automation is achieved by data-driven parameter optimization and introduction of several evaluation metrics. We evaluated the performance by comparing with three other techniques, four quality metrics, and two realistic synthetic images and one real CLSM image.
566220
Endomorphic modelling for two-dimensional time-varying autoregressive model signals
Physical signals are often non-stationary. We assume that images are non-stationary and each pixel is the output of a different set of two-dimensional autoregressive (AR) model coefficients; therefore, texture cannot be characterised by applying AR modelling techniques to the entire image. A multistage modelling technique, which appears in the literature as "endomorphic modelling", is applied to this kind of signal. The signal is divided into sub-sets of samples and each sub-set is modelled using an AR model. A new image is formed using the same AR model coefficient estimated from different blocks, then the new image is re-modelled using AR models again. This process ends when the variance of the estimated parameters is sufficiently small. The simulation results show that the variances obtained from the estimated coefficients at the second stage are much smaller than the ones from the first stage
31361010
Mammogram Analysis Using Two-Dimensional Autoregressive Models: Sufficient or Not?
Two-dimensional (2–D) autoregressive (AR) models have been used as one of the methods to characterise the textures of tumours in mammograms. Previously, the 2–D AR model coefficients were estimated for the block containing the tumour and the blocks in its 3 × 3 neighbourhood. In this paper, the possibility of having the estimated set of AR model coefficients of the block containing the tumour as a unique set of AR model coefficients for the entire mammogram is looked into. Based on the information given from the MiniMammography database, the possible number of blocks of the same size of the block containing the tumour is obtained from the entire mammogram and for each block a set of AR model coefficients is estimated using a method that combines both the Yule-Walker system of equations and the Yule-Walker system of equations in the third-order statistical domain. These sets of AR model coefficients are then compared. The simulation results show that 98.6% of the time we can not find another set of AR model coefficients representing the blocks of pixels in the possible neighbourhood of the entire mammogram for the data (95 mammograms with 5 of them having two tumours) available in the MiniMammography database.
12759831
RITA: receiver initiated just-in-time tree adaptation for rich media distribution
Application-level multicast networks overlaid on unicast IP networks are increasingly gaining in importance. While there have been several proposals for overlay multicast networks, very few of them focus on the stringent requirements of real-time applications such as streaming media. We propose RITA (Receiver Initiated Timely Adaptation) framework for an efficient overlay multicast infrastructure. RITA is based on a combination of landmark clustering and RTT measurements, and is particularly suitable for multimedia real-time applications. Our goal is to balance the network-oriented goals of building an efficient multicast tree with the application-oriented goals of providing good QoS with minimal disruptions. Using accurate global soft state information tables, our approach promptly constructs and reconfigures high quality trees. A distinguishing feature of our approach is that the tree reconfiguration is initiated just-in-time by the application client at the receiver when the media quality falls below a specific threshold. The goal is to achieve dynamic tree reconfiguration with very low switching delay such that end users do not perceive any application performance degradation.
15947479
A new wireless ad hoc multicast routing protocol
An ad hoc network is a multi-hop wireless network of mobile nodes without the intervention of fixed infrastructure. Limited bandwidth and mobility require that ad hoc routing protocols be robust, simple, and energy conserving. This paper proposes a new ad hoc multicast routing protocol called neighbor-supporting multicast protocol (NSMP). NSMP adopts a mesh structure to enhance resilience against mobility. And NSMP utilizes node locality to reduce the overhead of route maintenance. NSMP also attempts to improve route efficiency and reduce data transmissions. Our simulation results show that NSMP delivers packets efficiently while substantially reducing control overhead in various environments.
9536
Current practices of leading e-government countries
IT is transforming the way governments function and valuable lessons can be learned from the pioneering e-government programs that have led the charge.