1,721,129 research outputs found
Information and Communication Technologies (ICT) to Improve Prescription Appropriateness and Therpy Adherence in the Multi-morbid Elderly
Full text linkMultimorbidity patients pose severe challenges to which information and communication technology (ICT) can help patients and doctors to answer with effective and efficient care
A New Real Time Filter for Local Exposure Correction in Panoramic Radiography
No full textA new real time filter for local exposure correction in panoramic radiographs is presented here. The filter, called PaRSEC, allows eliminating the exposure artifacts, mainly introduced by Automatic Exposure Control (AEC) systems. These artifacts reduce the image readability and its diagnostic utility. The PaRSEC filter operates a local exposure equalization, based on a reliable estimate of the column mean gray level. Qualitative and quantitative results are reported for typical panoramic radiographs. They show a complete removal of the artifacts. The method compares favorably with other classical methods targeted to exposure correctio
Learning to maintain upright posture: what can be learnt using adaptive neural networks models?
No full textHuman upright posture is an unstable position: Continuous activation of postural muscles is required
to avoid falling down. This is the output of a complex control system that monitors a very large
number of inputs, related to the orientation of the body segments, to produce an adequate output as
muscle activation. Complexity arises because of the very large number of correlated inputs and outputs:
The finite contraction and release time of muscles and the neural control loop delays make the
problem even more difficult. Nevertheless, upright posture is a capability that is learned in the first
year of life. Here, the learning process is investigated by using a neural network model for the controller
and the reinforcement learning paradigm. To this end, after creating a mechanically realistic digital
human body, a feedback postural controller is defined, which outputs a set of joint torques as a function
of orientation and rotation speed of the body segments. The controller is made up of a neural network,
whose “synaptic weights” are determined through trial-and-error (failure in maintaining upright
posture) by using a reinforcement learning strategy. No desired control action is specified nor particular
structure given to the controller. The results show that the anatomical arrangement of the skeleton
is sufficient to shape a postural control, robust against torque perturbations and noise, and flexible
enough to adapt to changes in the body model in a short time. Moreover, the learned kinematics
closely resembles the data reported in the literature; it emerges from the interaction with the environment,
only through trial-and-error. Overall, the results suggest that anatomical arrangement of the
body segments may play a major role in shaping human motor control
Compression and smart coding of offset ad gain maps for intraoral digital x-ray sensors
No full textThe response of indirect x-ray digital imaging sensors is often not homogenous on the entire surface area. In this case, calibration is needed to build offset and gain maps, which are used to correct the
sensor output. The sensors of new generation are equipped with an on-board memory, which serves to store these maps. However, because of its limited dimension, the maps have to be compressed before saving them. This step is critical because of the extremely high compression rate required.
The authors propose here a novel method to achieve such a high compression rate, without degrading the quality of the sensor output. It is based on quad tree decomposition, which performs an adaptive sampling of the offset and gain maps, matched with a RBF-based interpolation strategy.
The method was tested on a typical intraoral radiographic sensor and compared with traditional compression techniques. Qualitative and quantitative results show that the method achieves a higher
compression rate and produces images of superior quality. The method can be adopted also in different fields where a high compression rate is required
Soft Tissue Filtering
No full textA method is disclosed for enhancing the visibility of at least some features of a radiographic image, the features belonging to at least a first and a second category of features, the method including the steps of determining a histogram of the image, analyzing the histogram in order to determine a distinction between values of image elements that more likely show a feature of the first category and values of image elements that more likely show a feature of the second category, and applying a correction to at least some of the image elements, wherein an image element that, according to the determined distinction, more likely shows a feature of the first category is corrected differently than an image element that, according to the determined distinction, more likely shows a feature of the second category. An apparatus and a computer-readable data carrier are adapted for performing the above steps or for causing a processor to perform the above steps
Real-time accurate circle fitting with occlusions
No full textAccurate location of circles inside images is a common problem in many scientific fields. Traditional algorithms, based on fitting a parameterized model, cannot accurately determine the circle in presence of partial occlusions. A novel problem formulation, based on maximum likelihood, allows estimating circles in real-time with sub-pixel accuracy also when occlusions are present
Denoising and contrast enhancement in dental radiography
No full textThis chapter shows how large improvement in image quality can be obtained when radiographs are filtered using adequate statistical models. In particular, it shows that impulsive noise, which appears as random patterns of light and dark pixels on raw radiographs, can be efficiently removed. A switching median filter is used to this aim: failed pixels are identified first and then corrected through local median filtering. The critical stage is the correct identification of the failed pixels. We show here that a great improvement can be obtained considering an adequate sensor model and a principled noise distribution, constituted of a mixture of photon counting and impulsive noise with uniform distribution. It is then shown that contrast in cephalometric images can be largely increased using different grey levels stretching for bone and soft tissues. The two tissues are identified through an adequate mixture derived from histogram analysis, composed of two Gaussians and one inverted log-normal. Results show that both soft and bony tissues become clearly visible in the same image under a wider range of conditions. Both filters work in quasi-real time for images larger than five Mega-pixels
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