In radiation therapy, patient positioning uncertainty and organ motion require that a volume larger than the actual tumour (named the planning target volume [PTV]) be irradiated to ensure that the tumour receives the ...
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While level sets have demonstrated a great potential for 3D medicalimage segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation...
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While level sets have demonstrated a great potential for 3D medicalimage segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications. this paper presents a tool for 3D segmentation that relies on level-set surface models computed at interactive rates on commodity graphics cards (CPUs). the interactive rates for solving the level-set PDE give the user immediate feedback on the parameter settings, and thus users can tune three separate parameters and control the shape of the model in real time. We have found that this interactivity enables users to produce good, reliable segmentation, as supported by qualitative and quantitative results.
this paper presents a disease-oriented evaluation of two recent retinal image registration algorithms, one for aligning pairs of retinal images and one for simultaneously aligning all images in a set. medical conditio...
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this paper presents a disease-oriented evaluation of two recent retinal image registration algorithms, one for aligning pairs of retinal images and one for simultaneously aligning all images in a set. medical conditions studied include diabetic retinopathy, vein occlusion, and both dry and wet age-related macular degeneration. the multi-image alignment worked virtually flawlessly, missing only 2 of 855 images. Pairwise registration, the Dual-Bootstrap ICP algorithm, worked nearly as well, successfully aligning 99.5% of the image pairs having a sufficient set of common features and 78.5% overall. images of retinas having an edema and pairs of images taken before and after laser treatment proved the most difficult to register.
Intravascular ultrasound (IVUS) is a medical imaging technique that not only provides three-dimensional information about the blood vessel lumen and wall, but also directly depicts atherosclerotic plaque structure and...
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In this paper, we propose a hybrid similarity measure for 2D-3D image registration that is a weighted combination of an intensity-based image similarity measure and a point-based measure incorporating a single fiducia...
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In this paper, we propose a hybrid similarity measure for 2D-3D image registration that is a weighted combination of an intensity-based image similarity measure and a point-based measure incorporating a single fiducial marker. We evaluate its accuracy and robustness using gold-standard clinical spine image data. the use of one fiducial marker substantially improves registration accuracy and robustness.
Mutual Information is perhaps the most widely used multimodality image registration method. A crucial step in mutual information is the estimation of the probability density function (pdf). In most cases, the Parzen w...
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Currently, there is no reliable method to accurately predict the outcome of breast reconstruction for an individual patient prior to surgery. To address this limitation, we are developing a simulation system capable o...
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Currently, there is no reliable method to accurately predict the outcome of breast reconstruction for an individual patient prior to surgery. To address this limitation, we are developing a simulation system capable of rendering patient-specific predictions of the post-operative breast shape. Such a system for the planning of breast reconstructive surgery will greatly enhance patient prognosis. In this paper, we present our computer simulations representing the reconstructed breast using idealized and realistic geometric models.
We present a computational approach to support gestational age determination of premature newborns. this knowledge is fundamental to guide postnatal treatment and increase survival chances. However, current approaches...
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ISBN:
(数字)9783540399032
ISBN:
(纸本)9783540204640
We present a computational approach to support gestational age determination of premature newborns. this knowledge is fundamental to guide postnatal treatment and increase survival chances. However, current approaches are both invasive and do not generate precise results. this paper presents an original and non-invasive method to determine the gestational age based on information supplied by plantar surface images. these images present many details and patterns but to date have not received attention from the image processing community. We provide a computational tool with suitable facilities to allow the image analysis, either automatically or user-driven. Besides its importance to Neonatology, this work represents a contribution of computer Vision concepts to medical Imaging. the experimental results confirm the relationship between the plantar surface features and newborn gestational age.
Accurate quantification of total body and the distribution of regional adipose tissue using manual segmentation is a challenging problem due to the high variation between manual delineations. Manual segmentation also ...
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Accurate quantification of total body and the distribution of regional adipose tissue using manual segmentation is a challenging problem due to the high variation between manual delineations. Manual segmentation also requires highly trained experts with knowledge of anatomy. We present a hybrid segmentation method that provides robust delineation results for adipose tissue from whole body MRI scans. A formal evaluation of accuracy of the segmentation method is performed. this semi-automatic segmentation algorithm reduces significantly the time required for quantification of adipose tissue, and the accuracy measurements show that the results are close to the ground truth obtained from manual segmentations.
this paper describes a novel method for simulating soft tissue deformation withimage-based rendering. It is based on the association of a depth map withthe texture image and the incorporation of micro-surface detail...
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this paper describes a novel method for simulating soft tissue deformation withimage-based rendering. It is based on the association of a depth map withthe texture image and the incorporation of micro-surface details to generate photorealistic images representing soft tissue deformations. In a preprocessing step, the depth map describing the surface is separated into two distributions corresponding to macro- and micro-surface details. During user interactive simulation, deformations resulting from tissue-instrument interaction are rapidly calculated by modifying a coarse mass-spring model fitted to the macrosurface structure. Micro-surface details are subsequently augmented to the modified model with 3D image warping. the proposed technique drastically reduces the polygonal count required to model the scene whilst preserving deformed small surface details to offer a high level of photorealism.
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