Nuclear Medical Image Analysis and Modeling
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
All areas of ionizing radiation detection - detectors, signal processing, analysis of results, PET development, PET results, medical imaging using ionizing radiation
We solicit high-quality original research papers (including significant work-in-progress) on any aspect of bioinformatics, biomedicine and healthcare informatics. New computational techniques and methods in machine learning; data mining; text analysis; pattern recognition; knowledge representation; databases; data modeling; combinatorics; stochastic modeling; string and graph algorithms; linguistic methods; robotics; constraint satisfaction; data visualization; parallel computation; data integration; modeling and simulation and their application in life science domain are especially encouraged.
Conference, workshops, and exhibits focusing on cooperation/linkages in the areas of Health Care Engineering (Applied biomedical and clinical eng.), Medical Information Technologies, and Medicine (patient care).
2011 International Conference on Grey Systems and Intelligent Services (GSIS 2011)
2011 IEEE International Conference on Grey Systems and Intelligent Services (GSIS'2011) focuses on current research on grey theory, systems, and rapidly advancing technologies in business improvement, business process automation, information management, and intelligent services.
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Science and technology related to the basic physics and engineering of magnetism, magnetic materials, applied magnetics, magnetic devices, and magnetic data storage. The Transactions publishes scholarly articles of archival value as well as tutorial expositions and critical reviews of classical subjects and topics of current interest.
Imaging methods applied to living organisms with emphasis on innovative approaches that use emerging technologies supported by rigorous physical and mathematical analysis and quantitative evaluation of performance.
All aspects of the theory and applications of nuclear science and engineering, including instrumentation for the detection and measurement of ionizing radiation; particle accelerators and their controls; nuclear medicine and its application; effects of radiation on materials, components, and systems; reactor instrumentation and controls; and measurement of radiation in space.
IEEE Transactions on Medical Imaging, 1998
In this paper implicit representations of deformable models for medical image enhancement and segmentation are considered. The advantage of implicit models over classical explicit models is that their topology can be naturally adapted to objects in the scene. A geodesic formulation of implicit deformable models is especially attractive since it has the energy minimizing properties of classical models. The aim ...
IEEE Transactions on Medical Imaging, 2012
We explore the application of genetic algorithms (GA) to deformable models through the proposition of a novel method for medical image segmentation that combines GA with nonconvex, localized, medial-based shape statistics. We replace the more typical gradient descent optimizer used in deformable models with GA, and the convex, implicit, global shape statistics with nonconvex, explicit, localized ones. Specifically, we propose ...
IEEE Transactions on Medical Imaging, 1997
The recovery of a three-dimensional (3-D) model from a sequence of two- dimensional (2-D) images is very useful in medical image analysis. Image sequences obtained from the relative motion between the object and the camera or the scanner contain more 3-D information than a single image. Methods to visualize the computed tomograms can be divided into two approaches: the surface ...
IEEE Transactions on Medical Imaging, 2010
The general linear model (GLM) is a well established tool for analyzing functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM proceed in a massively univariate fashion where the same design matrix is used for analyzing data from each voxel. A major limitation of this approach is the locally varying nature of signals of interest as well as ...
IEEE Transactions on Medical Imaging, 2017
Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such ...
How Facial Analysis Technology Can Help Children with Genetic Disorders - IEEE Region 4 Technical Presentation
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2011 IEEE Medal for Innovations in Healthcare Technology - Harrison H. Barrett
Dr. Scott Fish
The Benefits of Using IEEE Nuclear Standards: A Multi-Stakeholder View (webinar)
IEEE Life Scences - Paolo Bonato Interview
Engineering in Medicine and Biology: Segment 3
Classifying attention in Pivotal Response Treatment Videos - Corey Heath - LPIRC 2018
Larson Collection interview with Alvin Weinberg
Behavioral Signal Processing: Enabling human-centered behavioral informatics
IEEE John von Neumann Medal - Patrick Cousot - 2018 IEEE Honors Ceremony
Bringing Biological Models to Life: The Power of Agent-based Modeling and Visualization
Michael Johnson: Big Data in Healthcare
IMS 2015: Robert H. Caverly - Aspects of Magnetic Resonance Imaging
Hamid R Tizhoosh - Fuzzy Image Processing
Ignite! Session: Bill Moses
P2020 Establishing Image Quality Standards for Automotive
Neural Processor Design Enabled by Memristor Technology - Hai Li: 2016 International Conference on Rebooting Computing
IROS TV 2019- Rutgers University- Center for Accelerated Real Time Analytics
In this paper implicit representations of deformable models for medical image enhancement and segmentation are considered. The advantage of implicit models over classical explicit models is that their topology can be naturally adapted to objects in the scene. A geodesic formulation of implicit deformable models is especially attractive since it has the energy minimizing properties of classical models. The aim of this paper is twofold. First, a modification to the customary geodesic deformable model approach is introduced by considering all the level sets in the image as energy minimizing contours. This approach is used to segment multiple objects simultaneously and for enhancing and segmenting cardiac computed tomography (CT) and magnetic resonance images. Second, the approach is used to effectively compare implicit and explicit models for specific tasks. This shows the complementary character of implicit models since in case of poor contrast boundaries or gaps in boundaries, e.g. due to partial volume effects, noise, or motion artifacts, they do not perform well, since the approach is completely data-driven.
We explore the application of genetic algorithms (GA) to deformable models through the proposition of a novel method for medical image segmentation that combines GA with nonconvex, localized, medial-based shape statistics. We replace the more typical gradient descent optimizer used in deformable models with GA, and the convex, implicit, global shape statistics with nonconvex, explicit, localized ones. Specifically, we propose GA to reduce typical deformable model weaknesses pertaining to model initialization, pose estimation and local minima, through the simultaneous evolution of a large number of models. Furthermore, we constrain the evolution, and thus reduce the size of the search-space, by using statistically-based deformable models whose deformations are intuitive (stretch, bulge, bend) and are driven in terms of localized principal modes of variation, instead of modes of variation across the entire shape that often fail to capture localized shape changes. Although GA are not guaranteed to achieve the global optima, our method compares favorably to the prevalent optimization techniques, convex/nonconvex gradient- based optimizers and to globally optimal graph-theoretic combinatorial optimization techniques, when applied to the task of corpus callosum segmentation in 50 mid-sagittal brain magnetic resonance images.
The recovery of a three-dimensional (3-D) model from a sequence of two- dimensional (2-D) images is very useful in medical image analysis. Image sequences obtained from the relative motion between the object and the camera or the scanner contain more 3-D information than a single image. Methods to visualize the computed tomograms can be divided into two approaches: the surface rendering approach and the volume rendering approach. In this paper, a new surface rendering method using optical flow is proposed. Optical flow is the apparent motion in the image plane produced by the projection of real 3-D motion onto the 2-D image. The 3-D motion of an object can be recovered from the optical-flow field using additional constraints. By extracting the surface information from 3-D motion, it is possible to obtain an accurate 3-D model of the object. Both synthetic and real image sequences have been used to illustrate the feasibility of the proposed method. The experimental results suggest that the proposed method is suitable for the reconstruction of 3-D models from ultrasound medical images as well as other computed tomograms.
The general linear model (GLM) is a well established tool for analyzing functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM proceed in a massively univariate fashion where the same design matrix is used for analyzing data from each voxel. A major limitation of this approach is the locally varying nature of signals of interest as well as associated confounds. This local variability results in a potentially large bias and uncontrolled increase in variance for the contrast of interest. The main contributions of this paper are two fold: 1) we develop a statistical framework that enables estimation of an optimal design matrix while explicitly controlling the bias variance decomposition over a set of potential design matrices and 2) we develop and validate a numerical algorithm for computing optimal design matrices for general fMRI data sets. The implications of this framework include the ability to match optimally the magnitude of underlying signals to their true magnitudes while also matching the “null” signals to zero size thereby optimizing both the sensitivity and specificity of signal detection. By enabling the capture of multiple profiles of interest using a single contrast (as opposed to an F-test) in a way that optimizes for both bias and variance enables the passing of first level parameter estimates and their variances to the higher level for group analysis which is not possible using F-tests. We demonstrate the application of this approach to in vivo pharmacological fMRI data capturing the acute response to a drug infusion, to task-evoked, block design fMRI and to the estimation of a haemodynamic response function (HRF) in event-related fMRI. Although developed with motivation from fMRI, our framework is quite general and has potentially wide applicability to a variety of disciplines.
Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D high-resolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-the-art landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy.
Non-rigid registration has been widely used in medical image processing for many years. In order to preserve the anatomical topology and perform the registration more realistically and reliably for image guided surgery, methods based on statistical deformation model have been receiving considerable interests. However, the shortcomings in previous work such as the empirically configured weighting parameter for the statistical term lead to a controversial and unrealistic alignment. Therefore, a non-parametric method based on statistical deformation model is proposed here to avoid the discussion of weighting parameter. Our novel method is developed through incorporating the statistical model into two indispensable terms: similarity metric and smoothing regularizer. The advantages of the proposed algorithm in terms of convergence rate and registration accuracy have been proved mathematically in methodology and evaluated numerically in experiments compared with the state of the art method. It has also laid a solid foundation for the development of multi-modality image fusion with prior knowledge in the future.
There is growing clinical demand for image registration techniques that allow multimodal data fusion for accurate targeting of needle biopsy and ablative prostate cancer treatments. However, during procedures where transrectal ultrasound (TRUS) guidance is used, substantial gland deformation can occur due to TRUS probe pressure. In this paper, the ability of a statistical shape/motion model, trained using finite element simulations, to predict and compensate for this source of motion is investigated. Three-dimensional ultrasound images acquired on five patient prostates, before and after TRUS- probe-induced deformation, were registered using a nonrigid, surface-based method, and the accuracy of different deformation models compared. Registration using a statistical motion model was found to outperform alternative elastic deformation methods in terms of accuracy and robustness, and required substantially fewer target surface points to achieve a successful registration. The mean final target registration error (based on anatomical landmarks) using this method was 1.8 mm. We conclude that a statistical model of prostate deformation provides an accurate, rapid and robust means of predicting prostate deformation from sparse surface data, and is therefore well-suited to a number of interventional applications where there is a need for deformation compensation.
The period of the Medical Image Display and Analysis Group (MIDAG) so far is 1974-2002: more than 27 years. We began with a focus on two-dimensional (2-D) display: contrast enhancement, display scale choice, and display device standardization. We co-invented adaptive histogram equalization and later improved it to contrast-limited AHE, and we were perhaps the first to show that adaptive contrast enhancement, i.e., care in the mapping between recorded and displayed intensity and variation of that mapping with the local properties of the image, could significantly affect diagnostic or therapeutic decisions. MIDAG prides itself in having affected medical practice and, thus, the lives of patients. Despite the fact that bringing research from conception to actual medical use is a process sometimes taking a decade, the largest fraction, perhaps all, of our graduate students and faculty are attracted to these applications of computers by this altruism. Areas in which MIDAG research has come to this fruition are the uses of color display in nuclear medicine, the standardization of CRT display and the realization of how many bits of intensity are needed, and the use of tested contrast enhancement methods in areas of medical image use where subtle changes must be detected. Medical areas where we have had an effect are mammography, a major target area for both the standardization and contrast enhancement ends, and portal imaging in radiotherapy, a target area for contrast enhancement. In the 1980s, some of MIDAG's attention moved to image analysis. Also beginning in the 1980s we began to make some contributions to the notions of scale space description of images. With emphasis on the development of segmentation by deformable models and our aforementioned principle that validation is a critical part of research developing image analysis and display methods, we have begun to seriously face the issues of how to validate segmentation and how to choose the parameters of a segmentation method. Our experimental design and analysis techniques involve a variety of new methods for repeated variables designs.
This work deals with the use of a probabilistic quad-tree graph (Hidden Markov Tree, HMT) to provide fast computation, improved robustness and an effective interpretational framework for image analysis and processing in oncology. Thanks to two efficient aspects (multi observation and multi resolution) of HMT and Bayesian inference, we exploited joint statistical dependencies between hidden states to handle the entire data stack. This new flexible framework was applied first to mono modal PET image denoising taking into consideration simultaneously the Wavelets and Contourlets transforms through multi observation capability of the model. Secondly, the developed approach was tested for multi modality image segmentation in order to take advantage of the high resolution of the morphological computed tomography (CT) image and the high contrast of the functional positron emission tomography (PET) image. On the one hand, denoising performed through the wavelet-contourlet combined multi observation HMT led to the best trade-off between denoising and quantitative bias compared to wavelet or contourlet only denoising. On the other hand, PET/CT segmentation led to a reliable tumor segmentation taking advantage of both PET and CT complementary information regarding tissues of interest. Future work will investigate the potential of the HMT for PET/MR and multi tracer PET image analysis. Moreover, we will investigate the added value of Pairwise Markov Tree (PMT) models and evidence theory within this context.
Deformable models, which include deformable contours (the popular snakes) and deformable surfaces, are a powerful model-based medical image analysis technique. The authors develop a new class of deformable models by formulating deformable surfaces in terms of an affine cell image decomposition (ACID). The authors' approach significantly extends standard deformable surfaces, while retaining their interactivity and other desirable properties. In particular, the ACID induces an efficient reparameterization mechanism that enables parametric deformable surfaces to evolve into complex geometries, even modifying their topology as necessary. The authors demonstrate that their new ACID-based deformable surfaces, dubbed T-surfaces, can effectively segment complex anatomic structures from medical volume images.
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