A strategy for blood vessels segmentation based on the threshold which combines statistical and scale space filter. Application to the study of angiogenesis

This paper presents a strategy for segmenting blood vessels based on the threshold, which combines statistics and scale space filter. By incorporating statistical information, the strategy is capable of reducing over-segmentation. We propose a two-stage strategy which involves: (1) optimal selection of window size and (2) optimal selection of scale. We compared our strategy to two commonly used thresholding techniques. Experimental results showed that our method is much more robust and accurate. Our strategy suggested a modification to Otsu's method. In this application the important information to be extracted from images is only the number of blood vessels present in the images. The proposed segmentation technique is tested on manual segmentation, where segmentation errors less than 3% were observed. The work presented in this paper is a part of a global image analysis process. Therefore, these images will be subject to a further morphometrical analysis in order to diagnose and predict automatically malign tumors.     

Self-adaptation in next-generation Internet networks: how to react to traffic changes while respecting QoS?

Next-generation network infrastructure should support different services and several levels of quality of service (QoS) and resilience. The main requirements for such multiservice networks are flexibility, effective utilization of network resources, and ability to react to traffic demand changes with time. These requisites lead to the fact that next-generation networks should, to some extend, be "self-adapting," to cope with the need of promptly reacting to traffic demand changes, in an effective way, while taking into account the different requirements in terms of QoS. This paper sketches the reference network scenario and discusses the subject of self-adapting networks, attempting to focus on the key issues that need to be solved to realize future infrastructures. As a relevant example, this paper briefly reports a system developed by the authors' laboratory that implements a traffic engineering solution able to fulfill the mentioned requirements. This system could be regarded as a first concrete step toward the realization of self-adapting networks. In addition, some relevant results that were obtained either by simulation or by experiments on a real test bed are reported to discuss the main characteristics of such a system and assess the feasibility of the concept. Finally, the paper reviews the main hot issues that need to be addressed, in the authors' opinion, by the international research community.     

Dissipation in vibrating superleak second sound transducers

We have performed an experimental study of the generation and detection of second sound in⁴He using vibrating superleak second sound transducers. At temperatures well belowT and for low driving amplitudes, the magnitude of the generated second sound wave is proportional to the drive amplitude. However, nearT and for high drive amplitudes this is no longer the case—instead, the second sound amplitude saturates. In this regime we also find that overtones of the drive frequency are generated. Our results suggest that this behavior is due to critical velocity effects in the pores of the superleak in the generator transducer. This type of measurement may prove to be a useful way in which to study critical velocity effects in confined geometries.     

Model for small superconducting variable-thickness bridge constrictions

We present a simple model to simulate small three-dimensional superconducting constrictions of variable thickness (VTBs) for which the cross-sectional dimensions are of the same order as the length, and both are less than (T). We study the behavior of the modulus of the order parameterf and the supercurrent densityJ _s as a function of the various dimensions. We find that the extent to whichf is depressed in the banks depends on the ratio of width to length of the constriction. We show that even for very abrupt geometries,J _s is not zero andf does not reach its equilibrium value immediately outside the constriction. On the basis of our results, we propose a more general expression for the effective length valid also when all dimensions are of the same order. The calculated values ofdI _c /dT are consistent with experimental data from well-characterized VTBs. We suggest that the discrepancy between the calculated and experimental values ofI _c R _n may be due to the nonuniformity of the normal current density in the constriction.     

Expression of high-molecular-mass neurofilament protein in horse (Equus caballus) spinal ganglion neurons

Spinal ganglion (SG) neurons are subdivided, on the basis of their cytoplasmic aspect at light and electron microscopy, into dark (D) and light (L) neurons. Numerous efforts have been made to find specific markers able to identify D and L neuronal cytotypes. The isolectin B4 (IB4), utilized to identify nonpeptidergic D neurons in mice, unfortunately, has not proved as effective in other species. The 200-kDa neurofilament protein (NF200) is considered as a typical marker of L neurons in the rat, cat, and chick. The aim of this study was to analyze the histological, morphometric, and neurochemical characteristic of NF200-immunoreactive (IR) horse SG neurons, to better characterize them morphologically and functionally. NF200-IR neurons showed two levels (strong and weak) of staining intensity. Most (84%) strongly stained NF200-IR neurons corresponded to L neurons, and showed similar bimodality as in the size distribution study, which seems to indicate a third population of neurons, in addition to the two populations (small and large) previously identified. In triple-staining experiments where NF200 was colocalized with IB4, substance P (SP), and neuronal nitric oxide synthase (nNOS) neuronal markers, most NF200-IR neurons were single stained. On the contrary, most IB4-, SP-, and nNOS-stained neurons were triple labeled and almost equally subdivided between strong and weak NF200-IR with the latter being always smaller in size than strong NF200-IR neurons. In conclusion, horse SG neurons display significant morphometric and neurochemical differences compared with those of rodents.     

A Low‐Memory,Straightforward and Fast Bilateral Filter Through Subsampling in Spatial Domain

In this work we present a new algorithm for accelerating the colour bilateral filter based on a subsampling strategy working in the spatial domain. The base idea is to use a suitable subset of samples of the entire kernel in order to obtain a good estimation of the exact filter values. The main advantages of the proposed approach are that it has an excellent trade‐off between visual quality and speed‐up, a very low memory overhead is required and it is straightforward to implement on the GPU allowing real‐time filtering. We show different applications of the proposed filter, in particular efficient cross‐bilateral filtering, real‐time edge‐aware image editing and fast video denoising. We compare our method against the state of the art in terms of image quality, time performance and memory usage.     

An integrated computer-controlled system for assisting researchers in cortical excitability studies by using transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is the most important technique currently available to study cortical excitability. Additionally, TMS can be used for therapeutic and rehabilitation purposes, replacing the more painful transcranial electric stimulation (TES). In this paper we present an innovative and easy-to-use tool that enables neuroscientists to design, carry out and analyze scientific studies based on TMS experiments for both diagnostic and research purposes, assisting them not only in the practicalities of administering the TMS but also in each step of the entire study's workflow. One important aspect of this tool is that it allows neuroscientists to specify research designs at will, enabling them to define any parameter of a TMS study starting from data acquisition and sample group definition to automated statistical data analysis and RDF data storage. It also supports the diagnosing process by using on-line support vector machines able to learn incrementally from the diseases instances that are continuously added into the system. The proposed system is a neuroscientist-centred tool where the protocols being followed in TMS studies are made explicit, leaving to the users flexibility in exploring and sharing the results, and providing assistance in managing the complexity of the final diagnosis. This type of tool can make the results of medical experiments more easily exploitable, thus accelerating scientific progress.     

Estimation in X-bar control charts: effects and corrections

This paper presents an approach for improving the control limits of $ \overline{X} $ control charts when the parameters of the process are estimated and the control chart is in operation. In these conditions, the observed average run length (ARL) may be very different from the planned ARL since the parameter estimates may have a larger error. To minimize this problem, the data collected in effective control (phase 2) will be used to re-estimate the parameters with a precision greater than that obtained in phase 1. Thus, we defined a minimum sample size of observations of phase 2, which is constituted of a mixture of two normal distributions that should be used to re-estimate the process parameters. The proposal is illustrated with numerical example.