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A new method for visualisation and segmentation of vessel structures in 3D magnetic resonance angiography (MRA) images is presented. This method uses a simple statistical model of the information stored along parallel rays within the data set to derive a 2D projection image. Although similar to the maximum image projection (MIP) method, the new method uses a single parameter to achieve a higher contrast-to-noise...
Many medical image segmentation techniques have been proposed by lots of authors but they are mainly dedicated to particular solutions. There is no generic method for solving the image segmentation problem. The difficulty comes from that two types of noise are presented in medical images: physical noise due to the acquisition system, for example, Optical, X-rays and MRI, and physiological noise due...
It is significant to monitor bladder volume continuously and accurately in many specific clinical conditions or during treatment or management of urological disorders. This study try to explore the dynamic bladder storing process and understand the change of bladder shape and intra-vesical volume under the specific conditions of water-drinking using reconstructed three-dimension (3D) bladder models...
The segmentation of brain tissue from non-brain tissue in magnetic resonance (MR) images, commonly referred to as skull stripping, is an important image processing step in many neuroimage studies. In this paper, we propose a fast automatic skull-stripping method. The proposed method is based on an adaptive gauss mixture model and a 3D Mathematical Morphology method. The adaptive gauss mixture model...
MRI studies in post-traumatic stress disorder (PTSD) have focused primarily on manual based hippocampal volumetry, which is located in the subcortical. However, the cortical reduction or increase caused especially by thickness changes has not been well investigated. Recent advances in computational analysis provide new opportunities to use fully automatic techniques to measure cortical thickness,...
We address the difficult problem of segmenting the inflamed synovial tissue in multi-modal 3D MR sequences of the wrist. The complex morphology of the structures to segment, the dimensionality of the images, and the multiple modalities all contributes to the difficulty of the problem. We propose a solution based on a voxel classifier built on relatively few features selected from a large pool computed...
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...
Many analyses in neurosciences are carried out on histological and autoradiographic datasets and performed by manually drawing regions of interest on these 2D postmortem data. Such task being time-consuming, we propose an automated segmentation strategy to analyze 3D postmortem brain images. This method is based on the co-registration of a MRI-based 3D digital atlas on 3D-reconstructed postmortem...
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...
Image-guided therapy procedures require the patient to remain still throughout the image acquisition, data analysis and therapy. This imposes a tight time constraint on the over-all process. Automatic extraction of the pathological regions prior to the therapy can be faster than the customary manual segmentation performed by the physician. However, the image data alone is usually not sufficient for...
3D functional segmentation of brain images is important in understating the relationships between anatomy and mental diseases in brains. Volumetric analysis of various brain structures such as the cerebellum plays a critical role in studying the structural changes in brain regions as a function of development, trauma, or neurodegeneration. Although various segmentation methods in clinical studies...
This paper presents a fully automated symmetry-integrated brain injury detection method for magnetic resonance imaging (MRI) sequences. One of the limitations of current injury detection methods often involves a large amount of training data or a prior model that is only applicable to a limited domain of brain slices, with low computational efficiency and robustness. Our proposed approach can detect...
This paper presents a 3D non-rigid registration algorithm between histological and MR images of the prostate with cancer. To compensate for the loss of 3D integrity in the histology sectioning process, series of 2D histological slices are first reconstructed into a 3D histological volume. After that, the 3D histology-MRI registration is obtained by maximizing a) landmark similarity and b) cancer region...
The stiffness of the aortic wall was evaluated by coupling the morphological and the blood flow MR data. The deformability of the ascending aorta, and the aortic Pulse Wave Velocity (PWV) were estimated in a series of 36 volunteers aged between 14 and 77 years. An accurate automatic method of segmentation for extracting contours of the aorta was used. A 3D estimation of the length of the aortic arch...
The importance of accurate early diagnostics of dyslexia that severely affects the learning abilities of children cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We explore a possibility of distinguishing between dyslexic and normal (control) brains by a quantitative shape analysis of CWM gyrifications on 3D Magnetic...
The main objective of this paper is to provide an efficient tool for delineating brain tumors in three-dimensional magnetic resonance images. To achieve this goal, we use basically a region-based level-set approach and some conventional methods. Our proposed approach produces good results and decreases processing time. We present here the main stages of our system, and preliminary results which are...
Magnetic resonance imaging (MRI) is well adapted for early detection of diseases such as aortic aneuryms or dissections. In this paper, we present a new Markovian method which evolves an active contour for 2D, 3D and 4D (3D + time) segmentation. As opposed to other Markovian contour-based methods, our approach considers an implicit contour as the boundary of a 2D region. The regions are modeled via...
3D Brain SPECT imagery is a well established functional imaging method which has become a great help to physicians in the diagnosis of several neurological and cerebrovascular diseases. However, mainly due to the effects of attenuation and the scattering of emitted photons, inherent to this imaging process, 3D SPECT images are generally blurred and exhibit poor spatial resolution. This leads to substantial...
This paper proposes a new method to detect multiple sclerosis (MS) lesions on 3D multimodal brain MR images. MS lesions are detected as voxels that are not well explained by a statistical model for normal brain images. These outliers are extracted using the trimmed likelihood estimator (TLE). Spatial regularization is performed using a hidden Markov chain (HMC) model. Tests on real brain MR images...
We present an adaptive solution for guinea pig knee cartilage segmentation using a 3-D smoothing B-Spline active surface. An adaptive parametric combination of edge-based forces and balloon force solves the problem of capture range of external forces. The comparison between the results of the experiments using this method and previous 3-D validated snake segmentation shows that the accuracy and robustness...
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