Shape and function from motion in biomedical imaging: part 3.

This third and last paper in this series is devoted to biological imaging, e.g nano and micro imaging. Although a number of concerns are shared when going to these scales, the conditions of observation, the objects under study and the problems to address make this research very challenging by the cautions that must be taken to control the experiments, a mandatory condition to get conclusive results and to infer sound conclusions

An analysis of IEEE publications

INSPEC Accession Number:8979846This paper appears in: Engineering in Medicine and Biology Magazine, IEEE This study reports on a few elements of the scientific production of the IEEE. Other features could be displayed that would be interesting for a better understanding of the trajectories of the societies, journals, etc. The possibility of projecting new data onto the current spaces allows researchers to see if journals are static (the concepts and methods remaining stable) or dynamic (evolutions, ruptures can be tracked). In other words, this type of analysis can be used as a strategic tool to follow the impact and trends in engineering sciences. The fact that the authors concentrate on the IEEE publications prohibits any comparison with other societies publishing engineering papers. Such insights are feasible through the analysis of the INSPEC database. This could bring other clues on the coverage, the competition, and the reaction to new areas

Color Image Analysis by Quaternion-Type Moments

International audienceIn this paper, by using the quaternion algebra, the conventional complex-type moments (CTMs) for gray-scale images are generalized to color images as quaternion-type moments (QTMs) in a holistic manner. We first provide a general formula of QTMs from which we derive a set of quaternion-valued QTM invariants (QTMIs) to image rotation, scale and translation transformations by eliminating the influence of transformation parameters. An efficient computation algorithm is also proposed so as to reduce computational complexity. The performance of the proposed QTMs and QTMIs are evaluated considering several application frameworks ranging from color image reconstruction, face recognition to image registration. We show they achieve better performance than CTMs and CTM invariants (CTMIs). We also discuss the choice of the unit pure quaternion influence with the help of experiments. appears to be an optimal choice

Fast Computation of Sliding Discrete Tchebichef Moments and Its Application in Duplicated Regions Detection

International audienceComputational load remains a major concern when processing signals by means of sliding transforms. In this paper, we present an efficient algorithm for the fast computation of one-dimensional and two-dimensional sliding discrete Tchebichef moments. To do so, we first establish the relationships that exist between the Tchebichef moments of two neighboring windows taking advantage of Tchebichef polynomials’ properties. We then propose an original way to fast compute the moments of one window by utilizing the moment values of its previous window. We further theoretically establish the complexity of our fast algorithm and illustrate its interest within the framework of digital forensics and more precisely the detection of duplicated regions in an audio signal or an image. Our algorithm is used to extract local features of such a signal tampering. Experimental results show that its complexity is independent of the window size, validating the theory. They also exhibit that our algorithm is suitable to digital forensics and beyond to any applications based on sliding Tchebichef moments

3D nonrigid medical image registration using a new information theoretic measure.

International audienceThis work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen-Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions. A penalty term is incorporated into the objective function to smooth the nonrigid transformation. The goal of registration is to optimize an objective function consisting of a dissimilarity term and a penalty term, which would be minimal when two deformed images are perfectly aligned using the limited memory BFGS optimization method, and thus to get the optimal geometric transformation. To validate the performance of the proposed method, experiments on both simulated 3D brain MR images and real 3D thoracic CT data sets were designed and performed on the open source elastix package. For the simulated experiments, the registration errors of 3D brain MR images with various magnitudes of known deformations and different levels of noise were measured. For the real data tests, four data sets of 4D thoracic CT from four patients were selected to assess the registration performance of the method, including ten 3D CT images for each 4D CT data covering an entire respiration cycle. These results were compared with the normalized cross correlation and the mutual information methods and show a slight but true improvement in registration accuracy

Assessment of Left Ventricular Function in Cardiac MSCT Imaging by a 4D Hierarchical Surface-Volume Matching Process

Multislice computed tomography (MSCT) scanners offer new perspectives for cardiac kinetics evaluation with 4D dynamic sequences of high contrast and spatiotemporal resolutions. A new method is proposed for cardiac motion extraction in multislice CT. Based on a 4D hierarchical surface-volume matching process, it provides the detection of the heart left cavities along the acquired sequence and the estimation of their 3D surface velocity fields. A Markov random field model is defined to find, according to topological descriptors, the best correspondences between a 3D mesh describing the left endocardium at one time and the 3D acquired volume at the following time. The global optimization of the correspondences is realized with a multiresolution process. Results obtained on simulated and real data show the capabilities to extract clinically relevant global and local motion parameters and highlight new perspectives in cardiac computed tomography imaging

Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing.

International audienceIn abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors

Modélisation Markovienne pour l'estimation combinée de forme et de mouvement : Application au coeur en imagerie scanner multibarrette

Une méthode d'estimation conjointe de forme et de mouvement non rigide à partir de séquences temporelles tri-dimensionnelles est proposée. Basée sur une mise en correspondance surface-volume, elle permet, à partir d'une unique forme segmentée, d'estimer la forme et son mouvement sur toute la séquence. Une modélisation Markovienne combinée à un algorithme de recuit simulé estime les correspondances entre les noeuds du maillage modélisant l'objet à l'instant t et les voxels du volume à l'instant t + 1. La méthode a été appliquée à l'extraction de formes et de mouvements cardiaques en tomodensitométrie multibarrette. Les tests, réalisés sur données simulées et données réelles, ont donné des résultats prometteurs

Gamma regularization based reconstruction for low dose CT

International audienceReducing the radiation in computerized tomography is today a major concern in radiology. Low dose computerized tomography (LDCT) offers a sound way to deal with this problem. However, more severe noise in the reconstructed CT images is observed under low dose scan protocols (e.g. lowered tube current or voltage values). In this paper we propose a Gamma regularization based algorithm for LDCT image reconstruction. This solution provides a good balance between the regularizations based on l 0-norm and l 1-norm. We evaluate the proposed approach using the projection data from simulated phantoms and scanned Catphan phantoms. Qualitative and quantitative results show that the Gamma regularization based reconstruction can perform better in both edge-preserving and noise suppression when compared with other regularizations using integer norms