Javier Villanueva-Oller

ROI-based procedures for progressive transmission of digital images: A comparison

Nowadays, problems arise when handling large-sized images (i.e. medical image such as Computed Tomographies or satellite images) of 10, 50, 100 or more Megabytes, due to the amount of time required for transmitting and displaying, this time being even worse when a narrow bandwidth transmission medium is involved (i.e. dial-up or mobile network), because the receiver must wait until the entire image has arrived. To solve this issue, progressive transmission schemes are used. These schemes allow the image sender to encode the image data in such a way that it is possible for the receiver to perform a reconstruction of the original image from the very beginning of transmission. Despite this reconstruction being, of course, partial, it is possible to improve the reconstruction on the fly, as more and more data of the original image are received. There are many progressive transmission methods available, such as it planes, TSVQ, DPCM, and, more recently, matrix polynomial interpolation, Discrete Cosine Transform (DCT, used in JPEG) and wavelets (used in JPEG 2000). However, none of them is well suited, or perform poorly, when, in addition to progressive transmission, we want to include also ROIs (Region Of Interest) handling. In the progressive transmission of ROIs, we want not only to reconstruct the image as we receive image data, but also to be able to select which part or parts of the emerging image we think are relevant and want to receive first, and which part or parts are of no interest. In this context we present an algorithm for lossy adaptive encoding based on singular value decomposition (SVD). This algorithm turns out to be well suited for progressive transmission and ROI selection of 2D and 3D images, as it is able to avoid redundancy in data transmission and does not require any sort of data recodification, even if we select arbitrary ROIs on the fly. We compare the performing of SVD with DCT and wavelets and show the results.

Matrix Cubic Splines for Progressive 3D Imaging

Mathematical theory of matrix cubic splines is introduced, then adapted for progressive rendering of images. 2D subsets of a 3D digital object are transmitted progressively under some ordering scheme, and subsequent reconstructions using the matrix cubic spline algorithm provide an evolving 3D rendering. The process can be an effective tool for browsing three dimensional objects, and effectiveness is illustrated with a test data set consisting of 93 CT slices of a human head. The procedure has been implemented on a single processor PC system, to provide a platform for full 3D experimentation; performance is discussed. A web address for the complete, documented Mathematica code is given.

Progressive transmission of images: PC-based computations, using orthogonal matrix polynomials

Two methods for reconstructing a 3-D image as its 2-D parallel slices are transmitted progressively, in some order, are presented and analyzed. In the originating data base, an ordered set of 2-D slices could represent computer tomography (CT), magnetic resonance images (MRI), or cryosection cross-sections of a 3-D object, for example. With this digital formulation, matrix interpolating polynomial machinery renders a progressively-improving image as slices are received. A piecewise matrix polynomial reconstruction is also considered for reducing computational needs.

Using random networks to study the dynamics of respiratory syncytial virus (RSV) in the Spanish region of Valencia

Seasonal fluctuations in the incidence of several respiratory infections are a feature of epidemiological surveys all around the world. This phenomenon is characteristic of influenza and respiratory syncytial virus pandemics. However, the explanation of the seasonal outbreaks of these diseases remains poorly understood. Many statistical studies have been carried out in order to provide a correlation of the outbreaks with climatic or social factors without achieving a definitive conclusion. Here we show that, in a random social network, self-sustained seasonal epidemics emerge as a process modulated by the infection probability and the immunity period after recovering from the infection. This is a purely endogenous phenomenon that does not require any exogenous forcing. Assuming that this is the dominant mechanism for seasonal epidemics, many implications for public health policies for infectious respiratory diseases could be drawn.

Optimizing strategies for meningococcal C disease vaccination in Valencia (Spain)

BackgroundMeningococcal C (MenC) conjugate vaccines have controlled invasive diseases associated with this serogroup in countries where they are included in National Immunization Programs and also in an extensive catch-up program involving subjects up to 20 years of age. Catch-up was important, not only because it prevented disease in adolescents and young adults at risk, but also because it decreased transmission of the bacteria, since it was in this age group where the organism was circulating. Our objective is to develop a new vaccination schedule to achieve maximum seroprotection in these groups.MethodsA recent study has provided detailed age-structured information on the seroprotection levels against MenC in Valencia (Spain), where vaccination is routinely scheduled at 2 months and 6 months, with a booster dose at 18 months of age. A complementary catch-up campaign was also carried out in n for children from 12 months to 19 years of age. Statistical analyses of these data have provided an accurate picture on the evolution of seroprotection in the last few years.ResultsAn agent-based model has been developed to study the future evolution of the seroprotection histogram. We have shown that the optimum strategy for achieving high protection levels in all infants, toddlers and adolescents is a change to a 2 months, 12 months and 12 years of age vaccination pattern. If the new schedule were implemented in January 2014, high-risk subjects between 15-19 years of age would have very low seroprotection for the next 6 years, thereby threatening the program.ConclusionsHigh protection levels and a low incidence of meningococcal C disease can be achieved in the future by means of a cost-free change in vaccination program. However, we recommend a new catch-up program simultaneous to the change in regular vaccination program.

The effect of the Spanish Law of Political Parties (LPP) on the attitude of the Basque Country population towards ETA: A dynamic modelling approach

Abstract In June 2002, the Spanish Government passed the “Law of Political Parties” (LPP) with the aim, among others, of preventing parties giving political support to terrorist organizations. This law affected the Basque nationalist party “Batasuna”, due to its proved relation with ETA. In this paper, taking data from the Euskobarometro (Basque Country survey) related to the attitude of the Basque population towards ETA, we propose a dynamic model for the pre-LPP scenario. This model will be extrapolated to the future in order to predict what would have happened to the attitude of the Basque population if the law had not been passed. These model predictions will be compared to post-LPP data from the Euskobarometro using a bootstrapping approach in order to quantify the effect of the LPP on the attitude of Basque Country population towards ETA.

A probabilistic estimation and prediction technique for dynamic continuous social science models

In this paper, a computational technique to deal with uncertainty in dynamic continuous models in Social Sciences is presented. Considering data from surveys, the method consists of determining the probability distribution of the survey output and this allows to sample data and fit the model to the sampled data using a goodness-of-fit criterion based on the ?2-test. Taking the fitted parameters that were not rejected by the ?2-test, substituting them into the model and computing their outputs, 95% confidence intervals in each time instant capturing the uncertainty of the survey data (probabilistic estimation) is built. Using the same set of obtained model parameters, a prediction over the next few years with 95% confidence intervals (probabilistic prediction) is also provided. This technique is applied to a dynamic social model describing the evolution of the attitude of the Basque Country population towards the revolutionary organisation ETA.

SVD lossy adaptive encoding of 3D digital images for ROI progressive transmission

In this paper, we propose an algorithm for lossy adaptive encoding of digital three-dimensional (3D) images based on singular value decomposition (SVD). This encoding allows us to design algorithms for progressive transmission and reconstruction of the 3D image, for one or several selected regions of interest (ROI) avoiding redundancy in data transmission. The main characteristic of the proposed algorithms is that the ROIs can be selected during the transmission process and it is not necessary to re-encode the image again to transmit the data corresponding to the selected ROI. An example with a data set of a CT scan consisting of 93 parallel slices where we added an implanted tumor (the ROI in this example) and a comparative with JPEG2000 are given.

CASANDRA: A prototype implementation of a system of network progressive transmission of medical digital images

In this paper, a prototype for progressive transmission of medical digital 2D images through the network, called CASANDRA, is presented. The prototype consists of the server part and the client part. In the server part, the images are acquired, stored, computed their wavelet transform and the wavelet coefficients stored, then transmitted progressively, when required, via TCP to the client. In the client part, with the inverse wavelet transform, the received wavelet coefficients are used to build successive improved reconstructions of the image. This prototype has been implemented and is being tested in the Radiotherapy Service of the Valencia University Hospital (Valencia, Spain).

Random network models to predict the long term impact of HPV vaccination on genital warts

The Human papillomaviruses (HPV) vaccine induces a herd immunity effect in genital warts when a large number of the population is vaccinated. This aspect should be taken into account when devising new vaccine strategies, like vaccination at older ages or male vaccination. Therefore, it is important to develop mathematical models with good predictive capacities. We devised a sexual contact network that was calibrated to simulate the Spanish epidemiology of different HPV genotypes. Through this model, we simulated the scenario that occurred in Australia in 2007, where 12–13 year-old girls were vaccinated with a three-dose schedule of a vaccine containing genotypes 6 and 11, which protect against genital warts, and also a catch-up program in women up to 26 years of age. Vaccine coverage were 73% in girls with three doses and with coverage rates decreasing with age until 52% for 20–26 year-olds. A fast 59% reduction in the genital warts diagnoses occurred in the model in the first years after the start of the program, similar to what was described in the literature.