The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
It has been 60 years since the first knee implant surgery was performed yet questions concerning which material and implant design is most effective for specific populations still remain unanswered. There is a need for a faster, cost effective solution to test implants for their efficacy under physiological conditions. The primary objective of this project is to establish a workable design space for...
The spinal cord is a vital organ that serves as the only communication link between the brain and the various parts of the body. It is vulnerable to traumatic spinal cord injury and various diseases such as tumors, infections, inflammatory diseases and degenerative diseases. The exact segmentation and localization of the spinal cord are essential to effective clinical management of such conditions...
This paper proposes a novel combinational approach for statistical de-noising and segmentation of 3D magnetic resonance images (MRIs) of the brain. The proposed method is based on Markov Random Field (MRF), conjunction with simulated annealing (SA) and improved genetic algorithm (IGA). MRF methods have been widely studied for segmentation. Despite the Markovianity which depicts the local characteristic,...
This study proposes a new unsupervised approach for targets detection and classification in multispectral Magnetic Resonance (MR) images. The proposed method comprises two processes, namely Target Generation Process (TGP) and Constrained Energy Minimization (CEM). TGP is a fuzzy-set process that generates a set of potential targets from unknown information, and applies these targets to be desired...
Fuzzy inference systems are of great interest to provide a consistent mathematical framework for the representation of imprecision in relation to objects, relationships, knowledge and aims, and are viewed as powerful tools for reasoning and decision-making. In this paper, we survey several fuzzy approaches in magnetic resonances image processing, with an aim to develop and validate multidimensional...
Magnetic Resonance Imaging (MRI) has exhibited significant potential for quantifying cardiac function and dysfunction in the mouse. Recent advances in high-resolution cardiac MR imaging techniques have contributed to the development of acquisition approaches that allow fast and accurate description of anatomic structures, and accurate surface and finite element (FE) mesh model constructions for study...
The geometry of conduits derived from in vivo image data is subject to acquisition and reconstruction errors. This results in a degree of uncertainty in defining the bounding geometry for a patient-specific anatomical conduit. The impact of the conduit geometry uncertainty should be considered with respect to haemodynamic clinically relevant measures that may alter the perception and evaluation of...
The ordered electrical stimulation of the ventricles is achieved by a specialized network of fibres known as the Purkinje system. The gross anatomy and basic functional role of the Purkinje system is well understood. However, very little is known about the detailed anatomy of the Purkinje system, its inter-individual variability and the implications of the variability in ventricular function, in part...
Phase unwrapping is a classical problem in Magnetic Resonance Imaging (MRI), Interferometric Synthetic Aperture Radar and Sonar (InSAR/InSAS), fringe pattern analysis, and spectroscopy. Although many methods have been proposed to address this problem, robust and effective phase unwrapping remains a challenge. This paper presents a novel phase unwrapping method using a region-based Markov Random Field...
This paper investigates the applicability of multilevel macroscopic models for simulating solid tumor growth in the invasive glioblastoma multiforme (GBM) case. The continuum case approach tumor model based on the diffusion reaction equation is evaluated on a pre-segmented tomographic atlas where all tissue properties are known a priori. The atlas is further registered on a real clinical case where...
We propose a framework for the nonlinear spatiotemporal registration of 4D time-series of images based on the Diffeomorphic Demons (DD) algorithm. In this framework, the 4D spatiotemporal registration is decoupled into a 4D temporal registration, defined as mapping physiological states, and a 4D spatial registration, defined as mapping trajectories of physical points. Our contribution focuses more...
In this paper, image segmentation of brain magnetic resonance (MR) image is addressed in an unsupervised framework. We propose a novel method considering the hidden Markov random field model (HMRF) to model the image class labels, which takes into account the mutual influences of neighbouring sites formulated on the basis of fuzzy clustering principle. By introducing the effective means to incorporate...
The brain shape is deformed regionally by kinds of cerebral diseases and the degree of progress. Therefore quantitative evaluation of the deformation using MR images is effective for diagnosis of cerebral diseases. To evaluate the cerebral deformation, almost conventional methods are based on normalization of the brain shape which deforms the evaluating brain into the standardized brain. Because the...
Understanding the mechanisms of eye movement is difficult without a realistic biomechanical model. We present an efficient and robust computational framework for building subject-specific models of the orbit from magnetic resonance images (MRIs). We reconstruct three-dimensional geometric models of the major structures of the orbit (six extraocular muscles, orbital wall, optic nerve, and globe) by...
As magnetic resonance imaging (MRI) is an important technology of radiological evaluation and computer- aided diagnosis, the accuracy of the MR image segmentation directly influences the validity of following processing. The paper concerns medical image segmentation based on t-mixture model because of merits of the model. By analyzing the features of MR images, the main procedure of white matter segmentation...
Medical image segmentation and 3D mesh generation are the two critical challenges for numerical analysis based on medical images. Seamlessly linking different segmented results to appropriate mesh generation algorithms should be greatly beneficial for automatic and rapid finite element modeling from medical images. We present the interface representation models between segmentation and mesh generation...
It is difficult to segment MR images accurately due to factors such as noise contamination, intensive inhomogeneity and partial volume effect (PVE). In this paper, a fuzzy MRF model based on its conventional version is developed for the segmentation of MR images with intensive inhomogeneity. By solving the mathematic model, we derive a formula to compute the membership values for each voxel with respect...
We introduce a fluid mechanics based tractography method that estimates the most likely connection path between points in a tensor distribution function (TDF) dataset. We simulated the flow of an artificial fluid whose properties are related to the underlying TDF dataset. The resulting fluid velocity was used as a metric of connection strength. We validated our algorithm using a digital phantom dataset...
We propose a novel representation of shape variation using diffusion wavelets, and a search paradigm based on local features. The representation can reflect arbitrary and continuous interdependencies in the training data. In contrast to state-of-the-art methods our approach during the learning stage optimizes the coefficients as well as the number and the position of landmarks using geometric constraints...
Multi-atlas segmentation has been proved to perform well in segmenting sub-cortical structures from images. In this work, we study different components of multi-atlas segmentation and propose new techniques to improve the segmentation accuracy. We found that the use of gradient information in addition to standard normalised mutual information increases the registration accuracy. We also studied different...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.