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This survey on medical imaging provides a look into three major components. The first one deals with the full steps through which it must be apprehended: from the sensors to the reconstruction, from the image analysis up to its interpretation. The second aspect describes the physical principles used for imaging (magnetic resonance, acoustic, optics, etc.). The last section shows how imaging is involved in therapeutic procedures and in particular the new physical therapies. All along this paper, the research perspectives are sketched.

A moment-based approach is proposed for texture analysis of medical images. The neighborhood of texture pixel is calculated by moments for texture feature extraction. After verified on Brodatz textures, the moment-based texture analysis method is applied on CT liver scan classification and prostate ultrasound segmentation. A support vector machine and a multi-channel active contour model are used in our application. The result shows that our method works well but still have some limitations.

This work presents a novel method for multimodal medical registration based on histogram estimation of continuous image representation. The proposed method, regarded as “fast continuous histogram estimation,” employs continuous image representation to estimate the joint histogram of two images to be registered. The Jensen–Arimoto (JA) divergence is a similarity measure to measure the statistical dependence between medical images from different modalities. The estimated joint histogram is exploited to calculate the JA divergence in multimodal medical image registration. In addition, to reduce the grid effect caused by the grid-aligning transformations between two images and improve the implementation speed of the registration method, random samples instead of all pixels are extracted from the images to be registered. The goal of the registration is to optimize the JA divergence, which would be maximal when two misregistered images are perfectly aligned using the downhill simplex method, and thus to get the optimal geometric transformation. Experiments are conducted on an affine registration of 2D and 3D medical images. Results demonstrate the superior performance of the proposed method compared to standard histogram, Parzen window estimations, particle filter, and histogram estimation based on continuous image representation without random sampling. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new sequential weighted least squares (SWLS) method for positron emission tomography (PET) reconstruction. The SWLS algorithm is noniterative and can be considered as equivalent to the penalized WLS (PWLS) method under certain initial conditions. However, a full implementation of SWLS is computationally intensive. To overcome this problem, we propose a simplified SWLS as a reasonable alternative to the SWLS. The performance of this SWLS method is evaluated in experiments using both simulated and clinical data. The results show that the method can be advantageously compared with the original SWLS both in computation time and reconstruction quality. [Copyright &y& Elsevier]

The article describes initiatives to promote collaborative research between academics, clinicians and industrials in key areas of medical technology. Research and development spending in the medical technology industry is generally acknowledged to be between five and ten percent of sales. The European Commission and industry invest heavily in regular user training in medical technology. The five priority areas of medical informatics and telemedicine, medical imaging, biomedical instrumentation, biomaterials and techniques for disabled people have been identified and examined.

The article provides the basic formulations of moments. The authors review recently studied orthogonal moments including Tchebichef, Krawtchouk, Racah, dual Hahn moments, which all posses the property important in image processing, specifically in medical imaging. They discuss the different classification of moments: nonorthogonal moments, geometric moments, rotational moments, orthogonal moments, continuous orthogonal moments and discrete orthogonal moments, then sketch their different expressions, instead of presenting their theoretical details.

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. [ABSTRACT FROM AUTHOR]

Computational modeling and imaging in biology and medicine are gaining more and more interest with the discovery of in-depth structural and functional knowledge at all space and time scales (molecule to proteins, cells to organs and organizms). The recursion between description levels for highly dynamical, interacting and complex systems, i.e the integrative approach, is a very challenging topic where formal models, observational tools and experimental investigations have to be closely designed, coupled and confronted together. Imaging techniques play a major role in this interdisciplinary attempt to elucidate this biocomplexity: they convey relevant information about the underlying mechanisms, depict the conformations and anatomical topologies and draw the biophysical laws they may follow. Furthermore, the basic image analysis tools (from calibration to segmentation, motion estimation and registration up to pattern recognition) are generic enough to be of value whatever the objects under consideration. The same comments apply when Computer Graphics or Virtual Reality techniques are concerned. This paper will survey the recent contributions dealing with both models, imaging data and processing frames. Examples ranging over different scales, from macro to nano, will be given in order to enhance the mutual benefits and perspectives that can be expected from this coupling. [ABSTRACT FROM AUTHOR]

In this paper, a model-based approach to medical image analysis is presented. It is aimed at understanding the influence of the physiological (related to tissue) and physical (related to image modality) processes underlying the image content. This methodology is exemplified by modeling first, the liver and its vascular network, and second, the standard computed tomography (CT) scan acquisition. After a brief survey on vascular modeling literature, a new method, aimed at the generation of growing three-dimensional vascular structures perfusing the tissue, is described. A solution is proposed in order to avoid intersections among vessels belonging to arterial and/or venous trees, which are physiologically connected. Then it is shown how the propagation of contrast material leads to simulate time-dependent sequences of enhanced liver CT slices. [ABSTRACT FROM AUTHOR]

The article focuses on moment-based techniques in medical imaging. Edge detection is accomplished by derivative operators while integrative schemes may offer an alternative like in 2-D with geometric moments and Zernike moments. Geometric and Legendre moments have been proposed for 3-D surface detection that assume local planar patch or parabolic model. These techniques should be combined with other competitive methods to take advantage of their features such as invariance properties and computational complexity.

Reports on the Fifth IEEE Engineering in Medicine and Biology Society's International Summer School on Biomedical Imaging held at Berder's Island, Brittany, France on June 15-23, 2002. Organizers and supporters of the event; Forum for interdisciplinary exchange; Social activities.

The article discusses the highlights of the ninth Institute of Electrical and Electronics Engineers (IEEE) Engineering in Medicine and Biology Society (EMBS) Summer School that was held at Berder Island in Brittany, France, from June 18 to 26, 2010. Topics include stochastic-deformable model-based methods, medical image processing and how physics can help make new imaging techniques. The event featured several biomedical imaging experts including Mathias Fink, Dimitris Metaxas and Jerry Prince.

One-to-multiple medical image segmentation aims to directly test a segmentation model trained with the medical images of a one-domain site on those of a multiple-domain site, suffering from segmentation performance degradation on multiple domains. This process avoids additional annotations and helps improve the application value of the model. However, no successful one-to-multiple unsupervised domain adaptation (O2M-UDA) work has been reported in one-to-multiple medical image segmentation due to its inherent challenges: distribution differences among multiple target domains (among-target differences) caused by different scanning equipment and distribution differences between one source domain and multiple target domains (source–target differences). In this paper, we propose an O2M-UDA framework called dynamic domain adaptation (DyDA), for one-to-multiple medical image segmentation, which has two innovations: (1) dynamic credible sample strategy (DCSS) dynamically extracts credible samples from the target site and iteratively updates their number, thus iteratively expanding the generalization boundary of the model and minimizing the among-target differences; (2) hybrid uncertainty learning (HUL) reduces the voxel-level and domain-level uncertainty simultaneously, thus minimizing the source–target differences from the detail and entire perspective concurrently. Experiments on two one-to-multiple medical image segmentation tasks have been conducted to demonstrate the performance of the proposed DyDA. The proposed DyDA achieved competitive segmentation results and high adaptation with an average of 83.8% and 48.1% dice for the two tasks, respectively, which has improved by 21.7% and 9.2% compared with no adaptation, respectively. The code developed in this study code can be downloaded at https://github.com/ZoeyJiang/DyDA. [ABSTRACT FROM AUTHOR]

Minimal path method has been widely recognized as an efficient tool for extracting vascular structures in medical imaging. In a previous paper, a method termed minimal path propagation with backtracking (MPP-BT) was derived to deal with curve-like structures such as vessel centerlines. A robust approach termed CMPP (constrained minimal path propagation) is here proposed to extend this work. The proposed method utilizes another minimal path propagation procedure to extract the complete vessel lumen after the centerlines have been found. Moreover, a process named local MPP-BT is applied to handle structure missing caused by the so-called close loop problems. This approach is fast and unsupervised with only one roughly set start point required in the whole process to get the entire vascular structure. A variety of datasets, including 2D cardiac angiography, 2D retinal images and 3D kidney CT angiography, are used for validation. A quantitative evaluation, together with a comparison to recently reported methods, is performed on retinal images for which a ground truth is available. The proposed method leads to specificity (Sp) and sensitivity (Se) values equal to 0.9750 and 0.6591. This evaluation is also extended to 3D synthetic vascular datasets and shows that the specificity (Sp) and sensitivity (Se) values are higher than 0.99. Parameter setting and computation cost are analyzed in this paper. [ABSTRACT FROM AUTHOR]