Local energy, the pre-envelope, and filter resolution

We examine the construction of new filters for computing local energy, and compare these filters with the Gabor filters and the three-point-filter of Venkatesh [l]. Further, we demonstrate that the effect of convolution with complex Gabor filters is to band-pass (with some differentiating effect) and compute the local energy of the result. The magnitude of the resulting local energy is then used to detect features [2], [3] (step features, texture etc.), and the phase is used to classify the detected features [l], [4] or provide disparity information for stereo [5] and motion work [6], [7]. Each of these types of information can be obtained at multiple resolutions, enabling the use of course to fine strategies for computing disparity, and allowing the discrimination of image textures on the basis of which parts of the Fourier domain they dominate [8], [9].

Interobserver reliability of radial head fracture classification : two-dimensional compared with three-dimensional CT

Background: The Broberg and Morrey modification of the Mason classification of radial head fractures has substantial interobserver variation. This study used a large web-based collaborative of experienced orthopaedic surgeons to test the hypothesis that three-dimensional reconstructions of computed tomography (CT) scans improve the interobserver reliability of the classification of radial head fractures according to the Broberg and Morrey modification of the Mason classification.

Methods: Eighty-five orthopaedic surgeons evaluated twelve radial head fractures. They were randomly assigned to review either radiographs and two-dimensional CT scans or radiographs and three-dimensional CT images to determine the fracture classification, fracture characteristics, and treatment recommendations. The kappa multirater measure (κ) was calculated to estimate agreement between observers.

Results: Three-dimensional CT had moderate agreement and two-dimensional CT had fair agreement among observers for the Broberg and Morrey modification of the Mason classification, a difference that was significant. Observers assessed seven fracture characteristics, including fracture line, comminution, articular surface involvement, articular step or gap of ≥2 mm, central impaction, recognition of more than three fracture fragments, and fracture fragments too small to repair. There was a significant difference in kappa values between three-dimensional CT and two-dimensional CT for fracture fragments too small to repair, recognition of three fracture fragments, and central impaction. The difference between the other four fracture characteristics was not significant. Among treatment recommendations, there was fair agreement for both three-dimensional CT and two-dimensional CT.

Conclusions: Although three-dimensional CT led to some small but significant decreases in interobserver variation, there is still considerable disagreement regarding classification and characterization of radial head fractures. Three-dimensional CT may be insufficient to optimize interobserver agreement.

Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method

Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.

Parameter estimation of the BOLD fMRI model within a general particle filter framework

This work demonstrates a novel Bayesian learning approach for model based analysis of Functional Magnetic Resonance (fMRI) data. We use a physiologically inspired hemodynamic model and investigate a method to simultaneously infer the neural activity together with hidden state and the physiological parameter of the model. This joint estimation problem is still an open topic. In our work we use a Particle Filter accompanied with a kernel smoothing approach to address this problem within a general filtering framework. Simulation results show that the proposed method is a consistent approach and has a good potential to be enhanced for further fMRI data analysis.

Generation of total runoff hydrographs using a method derived from a digital filter algorithm

A reliable prediction of the total runoff hydrograph is necessary for water resources management. This study investigates two approaches to generate total runoff hydrograph by adding baseflow to direct runoff hydrographs. The first approach uses a method, derived from a digital filter algorithm for hydrograph separation, to generate baseflow hydrographs from direct runoff hydrographs. The method appears to perform well in producing the overall shape of the total runoff hydrographs and the acceptable mass balance errors for a year of water cycle. For application, the recession baseflow constant needs to be estimated reliably and the initial baseflow could be approximated to the long-term mean dry weather flow. The second approach assumes a constant baseflow rate. Although this approach is still capable of producing the overall hydrograph shape, it yields high mass balance errors in the total runoff hydrographs for both monthly and long-term periods. Further analysis shows that two-third of the mass balance errors are contributed from periods with direct runoff, implying that the constant baseflow assumption could introduce significant errors into the computations of total runoff hydrograph