Beamforming with sparse prior in ultrasound medical imaging

Nowadays the classical Delay-and-Sum (DAS) beamformer is extensively used in ultrasound imaging due to its low computational characteristics. However, it suffers from high sidelobe level, poor resolution and low contrast. An alternative is the Minimum-Variance (MV) beamformer which results in a higher image quality both in terms of spatial resolution and contrast. Even so, these benefits come at the expense of a higher computation complexity that limits its real-time capabilities. One solution that recently gained noticeable interest is the exploit of the sparsity of the scanned medium. Based on this assumption, we extend the DAS method to yield sparse results by using the Bayesian Information Criterion (BIC). Our realistic simulations demonstrate that the proposed beamforming (BF) method shows better performance than the classical DAS and MV in terms of lateral resolution, sidelobe reduction and contrast

A New Algorithm for Localized Motif Detection in Long DNA Sequences

The evolution in genome sequencing has known a spectacular growth during the last decade. One of the main challenges for the researchers is to understand the evolution of the genome and in particular to identify the DNA segments that have a biological significance. In this study we present a new algorithm -- ADMSL -- optimized for finding motifs in long DNA sequences and we emphasize some experiments done in order to evaluate the performance of the proposed algorithm in comparison with other motifs finding algorithms

Natural Language based On-demand Service Composition

The widespread of Web services in the ubiquitous computing era and the impossibility to predict a priori all possible user needs generates the necessity for on-demand service composition. Natural language is one of the the easiest ways for a user to express what he expects regarding a service. Two main problems need to be solved in order to create a composite service to satisfy the user: a)retrieval of relevant services and b) orchestration/composition of the selected services in order to fulfill the user request. We solve the first problem by using semantic concepts associated with the services and we define a conceptual distance to measure the similarity between the user request and a service configuration. Retrieved services are composed, based on aspect oriented templates called Aspects of Assembly. We have tested our application in an environment for pervasive computing called Ubiquarium, where our system composes a service according to the user request described by a sentence. The implementation is based on the WComp middleware that enables us to use regular Web services but also Web services for devices

Krevní analýza jako biometrický výběr veřejného klíče

V příspěvku se zabýváme technickým procesem ochrany lékařských informací a údajů s využitím technicky odvození veřejného klíče založeného na DNA pomocí krevní analýzy. Technika DNA šifrování je v příspěvku dále rozvedena a je ukázáno, jak jsou lékařská data osoby šifrována pomocí DNA vláken založených na principech molekulární biologie. Ochrana je zvýšena využitím hodnot úrovně minerálů v krvi pacienta jako základu pro výběr, přenos a regeneraci veřejného klíče této osoby.In this work we consider a technical process for protecting medical information and other data assets using a technique of deriving DNA public keys from blood analysis. A DNA encryption technique is further developed here in which a person’s medical data is encrypted in DNA strands based on the central dogma of molecular biology. Protection is enhanced by using a patient's own blood mineral levels as a seed for selecting, transmitting, and recovering that person’s public key

Brain Tumor Segmentation Based on Random Forest

In this article we present a discriminative model for tumor detection from multimodal MR images. The main part of the model is built around the random forest (RF) classifier. We created an optimization algorithm able to select the important features for reducing the dimensionality of data. This method is also used to find out the training parameters used in the learning phase. The algorithm is based on random feature properties for evaluating the importance of the variable, the evolution of learning errors and the proximities between instances. The detection performances obtained have been compared with the most recent systems, offering similar results

Elastic-net based beamforming in medical ultrasound imaging

International audienceThis paper presents a new way of addressing beamforming in ultrasound imaging, by formulating it, for each image depth, as an inverse problem solved using elastic-net regularization. This approach was evaluated on both simulated and in vivo data showing a gain in contrast, while maintaining an increased value of the signal-to-noise ratio compared to two standard ultrasound beamforming methods

Local Blur Assessment in Natural Images

International audienceThis paper presents a local no-reference blur assessment method in natural macro-like images. The purpose is to decide the blurriness of the object of interest. In our case, it represents the first step for a plant recognition system. Blur detection works on small non-overlapping blocks using wavelet decomposition and edge classification. At the block level the number of edges is less than on global images. A new set of rules is obtained by a supervised decision tree algorithm trained on a manually labelled base of 1500 blurred/un-blurred images. Our purpose is to achieve a qualitative decision of the blurriness/sharpness of the object of interest making it the first step towards a segmentation process. Experimental results show this method outperforms two other methods found in literature, even if applied on a block basis. Together with a pre-segmentation step, the method allows to decide if the object of interest (leaf, flower) is sharp in order to extract precise botanical key identification features (e. g. leaf border)

Local Blur Assessment in Natural Images

International audienceThis paper presents a local no-reference blur assessment method in natural macro-like images. The purpose is to decide the blurriness of the object of interest. In our case, it represents the first step for a plant recognition system. Blur detection works on small non-overlapping blocks using wavelet decomposition and edge classification. At the block level the number of edges is less than on global images. A new set of rules is obtained by a supervised decision tree algorithm trained on a manually labelled base of 1500 blurred/un-blurred images. Our purpose is to achieve a qualitative decision of the blurriness/sharpness of the object of interest making it the first step towards a segmentation process. Experimental results show this method outperforms two other methods found in literature, even if applied on a block basis. Together with a pre-segmentation step, the method allows to decide if the object of interest (leaf, flower) is sharp in order to extract precise botanical key identification features (e. g. leaf border)