CVT-based 2D motion planning with maximal clearance

Maximal clearance is an important property that is highly desirable in multi-agent motion planning. However, it is also inherently difficult to attain. We propose a novel approach to achieve maximal clearance by exploiting the ability of evenly distributing a set of points by a centroidal Voronoi tessellation (CVT). We adapt the CVT framework to multi-agent motion planning by adding an extra time dimension and optimize the trajectories of the agents in the augmented domain. As an optimization framework, our method can work naturally on complex regions. We demonstrate the effectiveness of our algorithm in achieving maximal clearance in motion planning with some examples.published_or_final_versionThe 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 9-13 May 2011. In Proceedings of the IEEE-ICRA, 2011, p. 2281-228

Statistical atlas based registration and planning for ablating bone tumors in minimally invasive interventions

Bone tumor ablation has been a viable treatment in a minimally invasive way compared with surgical resections. In this paper, two key challenges in the computer-Assisted bone tumor ablation have been addressed: 1) establishing the spatial transformation of patient's tumor with respect to a global map of the patient using a minimum number of intra-operative images and 2) optimal treatment planning for large tumors. Statistical atlas is employed to construct the global reference map. The atlas is deformably registered to a pair of intra-operative fluoroscopy images, constructing a patient-specific model, in order to reduce the radiation exposure to the sensitive patients such as pregnant and infants. The optimal treatment planning system incorporates clinical constraints on ablations and trajectories using a multiple objective optimization, which obtains optimal trajectory planning and ablation coverage using integer programming. The proposed system is presented and validated by experiments. © 2012 IEEE.published_or_final_versio

PCTV: A biologically- and psychologically-inspired edge and line detection

A novel method for detecting edges and lines simultaneously and automatically is proposed. This method, based on phase congruency and tensor voting (hence PCTV), makes use of the properties of how edges and lines are built from the Fourier decomposition of an image, and how the primary visual cortex responds to them, instead of making assumptions on the intensity profiles of the regions near a feature. Experiments showed that the detection results were more consistent to the "ground truth" manually drawn by humans. For detecting edges, this method is superior to three commonly used detectors in that it reduces the production of false detections. © 2010 IEEE.published_or_final_versionThe 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, 26-29 September 2010. In Proceedings of the 17th IEEE ICIP, 2010, p. 1621-162

Leucine-rich repeat kinase 2 mutations and Parkinson’s disease: three questions

Mutations in the gene encoding LRRK2 (leucine-rich repeat kinase 2) were first identified in 2004 and have since been shown to be the single most common cause of inherited Parkinson’s disease. The protein is a large GTP-regulated serine/threonine kinase that additionally contains several protein–protein interaction domains. In the present review, we discuss three important, but unresolved, questions concerning LRRK2. We first ask: what is the normal function of LRRK2? Related to this, we discuss the evidence of LRRK2 activity as a GTPase and as a kinase and the available data on protein–protein interactions. Next we raise the question of how mutations affect LRRK2 function, focusing on some slightly controversial results related to the kinase activity of the protein in a variety of in vitro systems. Finally, we discuss what the possible mechanisms are for LRRK2-mediated neurotoxicity, in the context of known activities of the protein

Surface Characterisation Based Tool Wear Monitoring in Peripheral milling

The progress of surface metrology in the last decade has led to improved 3D characterisation of surfaces which offers the possibility of monitoring manufacturing operations to give highly detailed information regarding the machine tool condition. This paper presents a case study where areal surface characterisation is used to monitor tool wear in peripheral milling. Due to the fact that tool wear has a direct effect on the machined workpiece surface, the machined surface topography contains much information concerning the machining conditions including the tool wear state. Through analysing the often subtle changes in the surface topography the tool wear state can be highlighted. This paper utilises areal surface characterization, areal auto-correlation function (AACF) and pattern analysis to illustrate the effect of tool wear on the workpiece surface. The result shows that: (1) tool wear, previously difficult to detect will influence almost all of the areal surface parameters; (2) the pattern features of AACF spectrum can reflect the subtle surface texture variation with increasing tool wear. The authors consider that, combined analysis of the surface roughness and its AACF spectrum are a good choice for monitoring the tool wear state especially with the latest developments in on-machine surface metrology

H-alpha +[NII] Observations of the HII Regions in M81

In a first of a series of studies of the H-alpha + [NII] emission from nearby spiral galaxies, we present measurements of H-alpha + [NII] emission from HII regions in M81. Our method uses large-field-CCD images and long-slit spectra, and is part of the ongoing Beijing-Arizona-Taipei-Connecticut Sky Survey. The CCD images are taken with the NAOC 0.6/0.9m f/3 Schmidt telescope at the Xinglong Observing Station, using a multicolor filter set. Spectra of 10 of the brightest HII regions are obtained using the NAOC 2.16m telescope with a Tek 1024 X 1024 CCD. The continua of the spectra are calibrated by flux-calibrated images taken from the Schmidt observations. We determine the continuum component of our H-alpha + [NII] image via interpolation from the more accurately-measured backgrounds (M81 starlight) obtained from the two neighboring (in wavelength) BATC filter images. We use the calibrated fluxes of H-alpha + [NII] emission from the spectra to normalize this interpolated, continuum-subtracted H-alpha + [NII] image. We estimate the zero point uncertainty of the measured H-alpha + [NII] emission flux to be $\sim$ 8%. A catalogue of H-alpha + [NII] fluxes for 456 HII regions is provided, with those fluxes are on a more consistent linear scale than previously available. The logarithmically-binned H-alpha + [NII] luminosity function of HII regions is found to have slope $\alpha$ = -0.70, consistent with previous results (which allowed $\alpha=-0.5 \sim -0.8$). From the overall H-alpha + [NII] luminosity of the HII regions, the star formation rate of M81 is found to be $\sim 0.68 M_{\odot} {\rm yr}^{-1}$, modulo uncertainty with extinction corrections.Comment: 18 pages, 7 figures, accepted for publication in the Astronomical Journa

Robustness and Accuracy of Feature-Based Single Image 2-D–3-D Registration Without Correspondences for Image-Guided Intervention

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Probabilistic segmentation of volume data for visualization using SOM-PNN classifier

We present a new probabilistic classifier, called SOM-PNN classifier, for volume data classification and visualization. The new classifier produces probabilistic classification with Bayesian confidence measure which is highly desirable in volume rendering. Based on the SOM map trained with a large training data set, our SOM-PNN classifier performs the probabilistic classification using the PNN algorithm. This combined use of SOM and PNN overcomes the shortcomings of the parametric methods, the nonparametric methods, and the SOM method. The proposed SOM-PNN classifier has been used to segment the CT sloth data and the 20 human MRI brain volumes resulting in much more informative 3D rendering with more details and less artifacts than other methods. Numerical comparisons demonstrate that the SOM-PNN classifier is a fast, accurate and probabilistic classifier for volume rendering.published_or_final_versio

Assessing 3D tunnel position in ACL reconstruction using a novel single image 3D-2D registration

Poster Session: 2D/3D and FluoroscopyConference Theme: Image-Guided Procedures, Robotic Interventions, and ModelingThe routinely used procedure for evaluating tunnel positions following anterior cruciate ligament (ACL) reconstructions based on standard X-ray images is known to pose difficulties in terms of obtaining accurate measures, especially in providing three-dimensional tunnel positions. This is largely due to the variability in individual knee joint pose relative to X-ray plates. Accurate results were reported using postoperative CT. However, its extensive usage in clinical routine is hampered by its major requirement of having CT scans of individual patients, which is not available for most ACL reconstructions. These difficulties are addressed through the proposed method, which aligns a knee model to X-ray images using our novel single-image 3D-2D registration method and then estimates the 3D tunnel position. In the proposed method, the alignment is achieved by using a novel contour-based 3D-2D registration method wherein image contours are treated as a set of oriented points. However, instead of using some form of orientation weighting function and multiplying it with a distance function, we formulate the 3D-2D registration as a probability density estimation using a mixture of von Mises-Fisher- Gaussian (vMFG) distributions and solve it through an expectation maximization (EM) algorithm. Compared with the ground-truth established from postoperative CT, our registration method in an experiment using a plastic phantom showed accurate results with errors of (-0.43°±1.19°, 0.45°±2.17°, 0.23°±1.05°) and (0.03±0.55, -0.03±0.54, -2.73±1.64) mm. As for the entry point of the ACL tunnel, one of the key measurements, it was obtained with high accuracy of 0.53±0.30 mm distance errors. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).published_or_final_versionSPIE Medical Imaging 2012, San Diego, CA., 4-9 February 2012. In Progress in Biomedical Optics and Imaging, 2012, v. 8316, art. no. 83162

Water Oxidation Catalysed by Iron Complex of N,N′-dimethyl-2,11-diaza[3,3](2,6)pyridinophane. Spectroscopy of Iron–oxo Intermediates and Density Functional Theory Calculations

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