A Homogeneous Algorithm for Motion Estimation and Compensation by Using Cellular Neural Networks

In this paper we present an original implementation of a homogeneous algorithm for motion estimation and compensation in image sequences, by using Cellular Neural Networks (CNN). The CNN has been proven their efficiency in real-time image processing, because they can be implemented on a CNN chip or they can be emulated on Field Programmable Gate Array (FPGA). The motion information is obtained by using a CNN implementation of the well-known Horn & Schunck method. This information is further used in a CNN implementation of a motion-compensation method. Through our algorithm we obtain a homogeneous implementation for real-time applications in artificial vision or medical imaging. The algorithm is illustrated on some classical sequences and the results confirm the validity of our algorithm

Compensation de mouvement par réseaux neuronaux cellulaires. Application en imagerie médicale

Ce travail concerne l\u27estimation et la compensation de mouvement à partir de séquences d\u27images. L\u27originalité de cette thèse consiste dans les solutions proposées pour l\u27implantation rapide de l\u27interpolation et de la compensation du mouvement sur un support physique existant de réseaux neuronaux cellulaires (RNC). Pour améliorer la précision des méthodes classiques d\u27estimation du mouvement, on a développé des approches avec prise en compte des discontinuités. Les algorithmes déterministes de minimisation de l\u27énergie par Maximum a Posteriori implantés sont l\u27ICM et le recuit en champ moyen. La compensation du mouvement implantée sur le RNC s\u27appuie sur le champ de mouvement estimé précédent. Ceci est facilité par la similitude entre le voisinage au sens de Markov et la structure physique des RNC. Les performances des algorithmes proposés, ont été testées sur des images médicales échographiques et tomographiques X. Les gains en temps obtenus sont de plusieurs ordres de grandeur (jusqu\u27à 103) et constituent une alternative extrêmement intéressante aux solutions conventionnelles

Medical diagnosis by using the motion information in image sequences

This paper consist in a comparative studybetween some differential motion estimation methodsthat could be applied in the case of medical diagnosisby using motion information in image sequences.The studied algorithms could be applied in the caseof the diagnosis of heart diseases, thyroid nodulardiseases, arteriosclerosis and other diseases thatimply the uses of image sequences. The paper aim tounderline some advantages and disadvantages ofseveral differential motion estimation methods inorder to allow to ease choose a certain motionestimation method for a certain application. Thestudied methods will be tested on MRI (MagneticResonance Imaging) images but the methods are notlimited only to this kind of data

Bond-graph Methods for Electric Circuits Analysis

The paper presents a bond-graph method for solving and analyzing an electric circuit with four or more circuit loops. Using this method, the time for circuit analysis is much shorter then using a classicalmethod. Besides determining the intensities of electrical currents through the sides of the circuit, the bond-graphs provide the possibility to obtain the transmittance of the analyzed system applying a fast working method. The main advantage of bond-graphs is the interaction with various areas of physics. The analyzedelectrical circuit could be a part of a complex physical system that could be modeled and analyzed as a unitary system by using bond-graphs

Current trends in scientific research in IT and Electrical and Electronics Engineering

From the vast research and development subjects in IT and Electrical and Electronics Engineering, some of the most important of them have been selected and are presented in this paper. Arguments that justify the chosen of these subjects among the most important in present are presented. The main selection criteria are the relevance for the current trends in these industries and the funding availability to insure the transition from theoretical and experimental research into practical applications. It might also help college students in shaping their preferences for a successful engineering carrier. Before approaching the specifics of the selected subjects the paper illustrates the interplay between learning strategies and scientific epistemological views to help framing an attitude toward science

Compensation de mouvement par réseaux neuronaux cellulaires (application en imagerie médicale)

Ce travail concerne l'estimation et la compensation de mouvement à partir de séquences d'images. L'originalité de cette thèse consiste dans les solutions proposées pour l'implantation rapide de l'interpolation et de la compensation du mouvement sur un support physique existant de réseaux neuronaux céllulaires (RNC). Pour améliorer la précision des méthodes classiques d'estimation du mouvement, on a développé des approches avec prise en compte des discontinuités. Les algorithmes déterministes de minimisation de l'énergie par Maximum a Posteriori implantés sont l'ICM et le recuit en champ moyen. La compensation du mouvement implantée sur le RNC s'appuie sur le champ de mouvement estimé précédent. Ceci est facilité par la similitude entre le voisinage au sens de Markov et la structure physique des RNC. Les performances de algorithmes proposés, ont été testées sur des imageries medicales échographiques et tomographiques X. Les gains de temps obtenus sont de plusieurs ordres de grandeur (jusqu'à 10 puissance 3) et constituent une alternative extrêment intéréssante aux solutions conventionnelles.This work concern motion estimation and compensation in image sequences. The originality of this thesis consist in the proposed solutions for a fast implementation of the interpolation and motion compensation on an existing hardware structure based on Cellular Neural Networks (CNN). To improve the precision of classical motion estimation methods, we developed markovian approaches, taking into account the discontinuities in the motion field. The deterministic algorithms implemented for the minimization of the maximum a Posteriori energy are ICM and mean-field annealing. The motion compensation implemented on CNN is based on the motion field, already estimated. This is facilitated by the similitude between the neighbourhood in the Markov sense and the physical structure of the CNN.VILLEURBANNE-DOC'INSA LYON (692662301) / SudocSudocFranceF

Current Role of Delta Radiomics in Head and Neck Oncology

The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or the extraction of data from various imaging modalities, is a tool often used to evaluate specific features related to the tumour or normal tissue that are not identifiable by the naked eye and which can add value to existing clinical data. Furthermore, the assessment of feature variations from one time point to another based on subsequent images, known as delta radiomics, was shown to have even higher value for treatment-outcome prediction or patient stratification into risk categories. The information gathered from delta radiomics can, further, be used for decision making regarding treatment adaptation or other interventions found to be beneficial to the patient. The aim of this work is to collate the existing studies on delta radiomics in head and neck cancer and evaluate its role in tumour response and normal-tissue toxicity predictions alike. Moreover, this work also highlights the role of holomics, which brings under the same umbrella clinical and radiomic features, for a more complex patient characterization and treatment optimisation

Performance Analysis of Turbo Codes, LDPC Codes, and Polar Codes over an AWGN Channel in the Presence of Inter Symbol Interference

This paper discusses the results of simulations relating to the performances of turbo codes, low density parity check (LDPC) codes, and polar codes over an additive white Gaussian noise (AWGN) channel in the presence of inter symbol interference, denoting the disturbances that altered the original signal. To eliminate the negative effects of inter symbol interference (ISI), an equalizer was used at the level of the receiver. Practically, two types of equalizers were used: zero forcing (ZF) and minimum mean square error (MMSE), considering the case of perfect channel estimation and the case of estimation using the least square algorithm. The performance measure used was the modification of the bit error rate compared to a given signal to noise ratio; in this sense, the MMSE equalizer offered a higher performance than the ZF equalizer. The aspect of channel equalization considered here is not novel, but there have been very few works that dealt with equalization in the context of the use of turbo codes, especially LDPC codes and polar codes for channel coding. In this respect, this research can be considered a contribution to the field of digital communications

Direct Image Registration with Adaptive Multi-Resolution

International audienc

An Adaptive Multi-Resolution Algorithm for Motion Estimation in Medical Image Sequences

International audienc