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In the wake of technological developments, new and exciting capacities and applications are being introduced in medical practice. Examples of this contribution are the progress of Minimally Invasive procedures and the new diagnostic techniques. The work and research of non health professionals, such as engineers, physicists, etc., and their different approaches to solving biomedical challenges from their technical point of view, while respecting clinical constraints, has been key for this contribution. Moreover, nowadays information is increasingly accessible to all, so that patients access the internet and can learn about all medical advances with a click on their computer. Thus they more easily accept all new health techniques and are readier to assume responsibility for caring for their own health. Thanks, in part, to the above suggested reasons, life expectancy is growing in the western countries, where improved medical procedures are implemented along with better diet. This has placed us in an scenario where people now aged less than 18 years old will probably live beyond a 100 years. Consequently, it is necessary that our society revises its professional retirement model, in order to preserve our health system and its sustainability. The slot of time where people now aged less than 18 years old reach over a 100 years old is what we have termed the 4th space. In this space, it is of utmost importance to teach people to manage their own life, health, economy, etc. They will be Health Services users for a very long time, so that their retirement should also be replanned for the sustainability of these Health Services. These people need to change their activities during their lifetimes, not only in terms of physical activities, but also their habits regarding the use of Health Systems. They will have automatic aid devices at home, and not just at the hospital. In the present paper, we discuss our views based on these ideas and on our expertise in clinical practice, education and technology development for health.

Everyone is an user of the Healthcare related services. Evolution of technology has changed the mode of practicing medicine, mainly, in surgical areas. In general, no one is wondering how the technology has contributed to the improvement of the medical procedures. In this paper, we propose some technologies used at present in other areas that could be used in the daily workflow of the hospitals, and how these technologies may contribute to the improvement of the physicians’ work and the health system. This idea emerges from our clinical and technological experience.

How is it possible that when talking about technological developments in surgery and specifically in endovascular surgery no one thinks that without the other sciences: engineers, physicists, ITs etc, we wouldnt have the evolution we have now?

Elastography measures the elastic properties of soft tissues using principally ultrasound (US) or magnetic resonance (MR) signals. The elastic behavior of tissues can be analyzed with tensor signal processing. In this work, we propose an analysis of elastography through the deformation tensor and its decomposition into both strain and vorticity tensors. The vorticity gives information about the rotation of the inclusions (simulated tumors) that might be helpful in the discrimination between malign and benign tumors without using biopsy. The tensor strain field visualizes in one image the standard scalar parameters that are usually represented separately in elastography. By using this technique physicians would have complementary information. In addition, it offers them the possibility of extracting new discriminant and useful parameters related to the elastic behavior of tissues. Although clinical validation is needed, synthetic experiments from finite element and ultrasound simulations present reliable results.

Ultrasound elastography measures the elastic properties of soft tissues using ultrasound signals. The elastic problem can be analyzed with tensor signal processing. In this work, we propose a new interpretation of elastography through the deformation tensor and its decomposition into both the strain and vorticity tensors. Vorticity gives information about the rotation of the inclusions that might help in the discrimination between malign and benign tumors without using biopsy. Although clinical validation is needed, synthetic experiments present reliable results.

Acquisition systems are not fully reliable since any real sensor will provide noisy and possibly incomplete and degraded data. Therefore, in tensor measurements, all problems dealt with in conventional multidimensional statistical signal processing are present with tensor signals. In this chapter we describe some noniterative approaches to tensor signal processing. Our schemes are achieved by the estimation of a local structure tensor, which is used as a key element in regularization. A stochastic point of view as well as a phase-invariant implementation are presented. This work also covers tensor extensions for common scalar operations such as anisotropic interpolation and filtering. An application of the structure tensor for regularization of deformation fields in tensor image registration is also shown. The techniques presented in this chapter suppose an alternative to variational and PDEs schemes, and another point of view of the tensor signal processing.

In this paper, we present a new non-iterative anisotropic method to regularize vector and higher order tensor fields. The driving application for this work is filtering data from diffusion tensor imaging. Our scheme is based on a multidimensional anisotropic adaptive filtering. The method is based on first estimating the local structure of the field and, then, combining the outputs of a set of steerable filter basis responses to the tensor field, which are adaptively weighted depending on the local structure. Promising results are presented from experiments on both synthetic data and real diffusion tensor MRI.

Diffusion tensor MRI (DT-MRI) is an image modality that is gaining clinical importance. After some preliminaries that describe the fundamentals of this imaging modality, we present a new technique to interpolate the diffusion tensor field, preserving boundaries and the constraint of positive-semidefiniteness. Our approach is based on the use of the inverse of the local structure tensor as a metric to compute the distance between the samples and the interpolation point. Interpolation is an essential step in managing digital image data for many different applications. Results on resampling (zooming) synthetic and clinical DT-MRI data are used to illustrate the technique. This new scheme provides a new framework for anisotropic image processing, including applications on filtering, registration or segmentation.

In this paper we address the problem of regularized data classification. To this extent we propose to regularize spatially the class-posterior probability maps, to be used by a MAP classification rule, by applying a non-iterative anisotropic filter to each of the class-posterior maps. Since the filter cannot guarantee that the smoothed maps preserve their probabilities meaning (i.e., probabilities must be in the range [0,1] and the class-probabilities must sum up to one), we project the smoothed maps onto a probability subspace. Promising results are presented for synthetic and real MRI datasets.

In this paper, we compare two restoration techniques applied to 3D angiographies and to femoral CT scans. The first technique uses a Partial Derivative Equation and the second one is based on an extension of adaptive Wiener filters. We first present each method. Then, we discuss and compare the estimation of the local orientations in 3D images obtained either by the smoothed gradient and the principal curvature directions or by the eigenvectors of the structure tensor. A good estimation of the orientations is essential because it directs the restoration process. Finally, we compare the restored images on both synthetic and real images for the two studied applications.

In this paper we describe our environment diSNei, a graphical tool for collaborative image analysis and visualization of models created out of slices of volume data; this application allows a number of users to simultaneous and coordinatedly analyze medical images, create graphical models, navigate through them and superimpose raw data onto the models. The application is intended to help physicians interpret data in the case that ambiguous situations may appear, by means of collaboration with other colleagues. It is therefore an integrated environment for expertise interchange among physicians and we believe that it is a powerful tool for academic purposes as well. Other outstanding application features are its being multiplatform, and, particularly, the fact that it can run on NT computers, and the support for stereo rendering so as to obtain a deep sensation of inmersion into the models.

We propose a new method for artifact reduction of upsampled multidimensional signals. These artifacts are evident near edges and they are due to the spectral narrowing associated with upsampling. The method is based on first upsampling the signal with conventional optimal sinc interpolation and then applying a local filter that reduces the high frequencies associated with the ringing artifacts along the edges while leaving unchanged the directions orthogonal to them. The method is specially suitable when dealing with medical images that contain small structures, such as thin bones in CT.

Computer-based technology has been sufficiently developed in medical training and education. However, as well as in other areas (flight pilots, nuclear energy operators, drivers, etc.) this technology has been included in other educational plans, in medicine it is a pending task yet. We present our work on the introduction of computer-based technology, mainly virtual simulation, into the university teaching plans at University of Las Palmas Medical School, the medicine residency programme and the update of physicians at the four Teaching Hospitals in Canary Islands. Through the realization of virtual simulation courses we have managed to introduce the technology into the health environment as an educational tool. Using commercial haptics devices and customizing and adapting its software, we have developed custom curricula for several medicine specialities.