Diffuse low-grade gliomas (DLGG) are brain tumors of young adults. They affect the quality of life of the inflicted patients and, if untreated, they evolve into higher grade tumors where the patient's life is at risk. Therapeutic management of DLGGs includes chemotherapy, and tumor diameter is particularly important for the follow-up of DLGG evolution. In fact, the main clinical basis for deciding whether to continue chemotherapy is tumor diameter growth rate. In order to reliably assist the doctors in selecting the most appropriate time to stop treatment, we propose a novel clinical decision support system. Based on two mathematical models, one linear and one exponential, we are able to predict the evolution of tumor diameter under Temozolomide chemotherapy as a first treatment and thus offer a prognosis on when to end it. We present the results of an implementation of these models on a database of 42 patients from Nancy and Montpellier University Hospitals. In this database, 38 patients followed the linear model and four patients followed the exponential model. From a training data set of a minimal size of five, we are able to predict the next tumor diameter with high accuracy. Thanks to the corresponding prediction interval, it is possible to check if the new observation corresponds to the predicted diameter. If the observed diameter is within the prediction interval, the clinician is notified that the trend is within a normal range. Otherwise, the practitioner is alerted of a significant change in tumor diameter.

To meet doctors' needs to store and share medical data remotely, lossy compression seems, today, to be an appropriate solution to manage the huge amount of these medical data. However, as there is a risk to lose critical medical information, experts' subjective quality reviews should be considered with respect to compression efficiency. In this context, we try to compare the last two video encoding international standards performances, taking into account quality assessment issues. Results show us that HEVC is more efficient than MPEG-4 AVC/H.264, offering up to 54% bit-rate saving comparing to AVC/H.264. Besides, we showed that ENT medical videos can be advantageously encoded in SD instead of Full HD resolution for low bit-rate applications. Finally, by comparison with doctors' perception, the appropriate objective metrics MSE, NQM, SSIM and MSSIM validate the previous results and confirm the superiority of HEVC over MPEG-4 AVC/H.264. They are very promising for telemedicine applications, especially in low bit-rate context.

It is admitted that lossy compression can be used in the field of medical images under the control of experts. Lossy compression can offer substantial reduction of the volumes of medical images, being thus an efficient solution for both storage and transmission problem in the medical context. Furthermore, the use of touchpads in medicine has grown and many medical applications on this kind of support is now available. The storage capacity of this kind of terminal is limited, lossy compression represents a good alternative to allow greedy medical applications on such terminals. In this work, we address the problem of quality assessment of MRI scans from an interactive upper limb radiology atlas (Monster Anatomy Upper Limb). The quality assessment protocol is adapted from the International Telecommunication Union recommendations (ITU-R BT-500-11). In this paper, we propose to determine compression thresholds which are acceptable according to the quality required for the proper use of this radiology atlas. We show that this application (using a simple JPEG encoder) has a lossy compression threshold ranging from 13: 1 for the majority of the atlas images up to 27: 1 for the hand images. Finally, several objective image quality assessment algorithms (IQA) are also linked to subjective ratings of the panel of health professionals.

We present a low computational cost bit allocation procedure dedicated to wavelet compression performed by entropy coded lattice vector quantization (ECLVQ). This approach is based on a previously proposed statistical model called multidimensional mixture of generalized Gaussian densities. Here, we focus on the distribution estimation step which requires to be as fast as possible. We show that the method of moments (MoM) can be used successfully as an alternative to Monte Carlo Markov chain approach (MCMC); this method allows not only to reduce the computational complexity but also to maintain a good estimation performance. Experimental results show the efficiency of our approach in terms of CPU time.

Entropy-coded lattice vector quantization (ECLVQ) with codebooks dedicated to independent identically distributed (i.i.d.) generalized Gaussian sources have proven their high coding performances in the wavelet domain. It is well known that wavelet coefficients with high magnitude (corresponding to edges and textures) tend to be clustered in a few amount of vectors. In this paper, we first show that this property has a major influence on the performances of ECLVQ schemes. Since this clustering property cannot be taken into account by the classical i.i.d. assumption, our first proposal is to model the joint distribution of vectors by a multidimensional mixture of generalized Gaussian (MMGG) densities. The main outcome of this MMGG model is to provide a theoretical framework to simply derive from i.i.d. R- D models, the corresponding MMGG R- D models. In a second part, a new codebook better suited to wavelet coding is proposed: the so-called dead zone lattice vector quantizers (DZLVQ). It consists of generalizing the scalar dead zone to vector quantization by thresholding vectors according to their energy. We show that DZLVQ improves the rate-distortion tradeoff. Experimental results are provided for the pyramidal LVQ scheme under the assumption of a multidimensional mixture of Laplacian (MML) densities. Results performed on a set of real life images show the precision of the analytical R- D curves and the efficiency of the DZLVQ scheme.

Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical applications.

In this paper, we present a new bit allocation procedure based on the approximation of the rate distortion (R-D) functions provided by our efficient lattice vector quantizer with pyramidal dead zone (DZLVQ). Here, we show that DZLVQ R-D functions can be efficiently fitted by an exponential model. This property leads to an analytical solution to the bit allocation problem which reduces significantly the complexity of our compression scheme. Furthermore, our method is highly parallelizable. Finally we show that it keeps the very good results in terms of visual quality of DZLQV, as it better preserves fine structures with respect to SPIHT and JPEG2000 at low rates.