Defective pelvic organ support is one of the most common problems in women and its intervention requires an accurate preoperative identification of prolapsed organs and their mechanical properties. With the current st...
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Defective pelvic organ support is one of the most common problems in women and its intervention requires an accurate preoperative identification of prolapsed organs and their mechanical properties. With the current state-of-the-art in magnetic resonance imaging, 3D dynamic imaging of the pelvic floor is prohibited by the maximal temporal resolution that can be achieved. In this paper, we propose an optimal scan-planning approach based on statistical shape modelling of the levators such that only a limited number of 2D imaging planes are needed to recover the entire 3D structure. The statistical shape model was achieved by the use of harmonic shape embedding and modelling whilst the reconstruction of the 3D structure from selected imaging planes was carried out by the incorporation of a conjunct MDL measure indicating the statistical shape variation captured by respective imaging planes. The value of the proposed technique was demonstrated with data acquired from a group of 11 asymptomatic subjects, highlighting the value of the method in studying dynamic shape changes when complete 3D imaging is impractical.
In this work we employ computer-vision techniques to detect natural biological symmetries in breast MR scans. Currently, breast MR images are assessed in terms of the kinetics and location of uptake of Gd-DTPA. Howeve...
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A fully-automated 3D image analysis method is proposed to segment lung nodules in HRCT. A specific gray-level mathematical morphology operator, the SMDC-connection cost, acting in the 3D space of the thorax volume is ...
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A fully-automated 3D image analysis method is proposed to segment lung nodules in HRCT. A specific gray-level mathematical morphology operator, the SMDC-connection cost, acting in the 3D space of the thorax volume is defined in order to discriminate lung nodules from other dense (vascular) structures. Applied to clinical data concerning patients with pulmonary carcinoma, the proposed method detects isolated, juxtavascular and peripheral nodules with sizes ranging from 2 to 20 mm diameter. The segmentation accuracy was objectively evaluated on real and simulated nodules. The method showed a sensitivity and a specificity ranging from 85% to 97% and from 90% to 98%, respectively.
An experimental system and interface that indicate the likelihood of deep venous thrombosis using objective measures was developed, based on ultrasound image processing using a modified Star-Kalman algorithm and a sen...
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An experimental system and interface that indicate the likelihood of deep venous thrombosis using objective measures was developed, based on ultrasound image processing using a modified Star-Kalman algorithm and a sensorized ultrasound probe. Force, location and image data is used to assess a vessel segment for compression. A user interface displays the results using a 3-D representation. A tissue phantom was developed for testing and validation. Initial results with this phantom and healthy volunteers are presented.
This paper deals with a new approach of registration in multimodal imaging. Modalities involved are Digital Subtracted Angiography (DSA, 2D) and Magnetic Resonance Angiography (MRA, 3D). Our approach is an hybrid one,...
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This paper deals with a new approach of registration in multimodal imaging. Modalities involved are Digital Subtracted Angiography (DSA, 2D) and Magnetic Resonance Angiography (MRA, 3D). Our approach is an hybrid one, mixing feature and intensity based approaches. This approach is based on the extraction of a anatomical referential common to both MRA and DSA. This step appears to be the "geometric-like" aspect. Then, a high level optimization scheme gives the best registration, using an iconic similarity measure. Several ways of matching planar and tomographic imaging are proposed through superimposition, point to point matching or 3D data fusion. The results obtained prove the methods efficiency in a clinical context.
Accurate reconstructions of vessels are important in the understanding of blood flow anomalies. We present a new elastic registration method to reconstruct the lumen of the femoral artery by fusion of Intravascular Ul...
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Accurate reconstructions of vessels are important in the understanding of blood flow anomalies. We present a new elastic registration method to reconstruct the lumen of the femoral artery by fusion of Intravascular Ultrasound data and biplane angiography. The catheter path and pullback parameters are found by minimizing width discrepancy in the back-projections without the need for explicit catheter segmentation. Preliminary results on real subjects are promising and show the convergence of the algorithm.
We propose a novel method for 3D image segmentation, where a Bayesian formulation, based on joint prior knowledge of the shape and the image gray levels, along with information derived from the input image, is employe...
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We propose a novel method for 3D image segmentation, where a Bayesian formulation, based on joint prior knowledge of the shape and the image gray levels, along with information derived from the input image, is employed. Our method is motivated by the observation that the shape of the object and the gray level variation in an image have consistent relations that provide configurations and context that aid in segmentation. We define a Maximum A Posteriori(MAP) estimation model using the joint prior information of the shape and image gray levels to realize image segmentation. We introduce a representation for the joint density function of the object and the image gray level values, and define joint probability distribution over the variations of object shape and the gray levels contained in a set of training images. By estimating the MAP shape of the object, we formulate the shape-appearance model in terms of level set function as opposed to landmark points of the shape. We found the algorithm to be robust to noise, able to handle multidi-mensional data, and avoiding the need for point correspondences during the training phase. Results and validation from various experiments on 2D/3D medicalimages are demonstrated.
To extend applicable area of minimally invasive neurosurgery, multi-DOF brain retract manipulator is developed. By inserting this manipulator deeper into the brain as an conductor and then other micromanipulator after...
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To extend applicable area of minimally invasive neurosurgery, multi-DOF brain retract manipulator is developed. By inserting this manipulator deeper into the brain as an conductor and then other micromanipulator afterwards, it will be possible to realize new minimally invasive neurosurgery. The brain retract manipulator system is controlled by new safety method with brain retraction pressure monitoring system, Tendon-driven unit which controls lock or free status of each joint of multi-joint spatula, Passive-hybrid control system that assure the system a suitable safety. In evaluation test with a hog, insertion of the multi-joint spatula as deep as the cerebral base was realized, and the possibility of securing the space between the temporal lobe and the cerebral base is verified.
Spatial normalization is a key process in cross-sectional studies of brain structure and function using MRI, fMRI, PET and other imaging techniques. A wide range of 3D image deformation algorithms have been developed,...
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In this paper we present an automatic statistical intensity based-approach for extracting the 3D cerebrovascular system from time-of-flight (TOP) magnetic resonance angiography (MRA) data. The voxels of the dataset ar...
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In this paper we present an automatic statistical intensity based-approach for extracting the 3D cerebrovascular system from time-of-flight (TOP) magnetic resonance angiography (MRA) data. The voxels of the dataset are classified as either background tissues, which are modeled by a finite mixture of one Rayleigh and two normal distributions, or blood vessels, which are modeled by one normal distribution. We show that the proposed models fit the clinical data properly and result in fewer misclassified vessel voxels. We estimated the parameters of each distribution using the expectation maximization (EM) algorithm. Since the convergence of the EM is sensitive to the initial estimate of the parameters, a novel method for parameter initialization, based on histogram analysis, is provided. A new geometrical phantom motivated by a statistical analysis was designed to validate the accuracy of our method. The algorithm was also tested on 20 in-vivo datasets. The results showed that the proposed approach provides accurate segmentation, especially those blood vessels of small sizes.
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