In this paper an automatic atlas-based segmentation algorithm for 4D cardiac MR images is proposed. The algorithm is based on the 4D extension of the expectation maximisation (EM) algorithm. The EM algorithm uses a 4D...
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ISBN:
(纸本)3540204628
In this paper an automatic atlas-based segmentation algorithm for 4D cardiac MR images is proposed. The algorithm is based on the 4D extension of the expectation maximisation (EM) algorithm. The EM algorithm uses a 4D probabilistic cardiac atlas to estimate the initial model parameters and to integrate a-priori information into the classification process. The probabilistic cardiac atlas has been constructed from the manual segmentations of 3D cardiac image sequences of 14 subjects. It provides space and time-varying probability maps for the left and right ventricle, the myocardium, and background structures such as the liver, stomach, lungs and skin. In addition to the probabilistic cardiac atlas, the segmentation algorithm incorporates spatial and temporal contextual information by using 4D Markov Random Fields (MRF). Validation against manual segmentations and computation of the correlation between manual and automatic segmentation on 249 3D volumes were calculated. Results show that the procedure can successfully segment the left ventricle (LV) (r=0.95), myocardium (r=0.83) and right ventricle (RV) (r=0.91).
Through an understanding of the image formation process, diagnostically important facts about the internal structure and composition of pigmented skin lesions can be derived from their colour images. A physics-based m...
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Through an understanding of the image formation process, diagnostically important facts about the internal structure and composition of pigmented skin lesions can be derived from their colour images. A physics-based model of tissue colouration provides a cross-reference between image colours and the underlying histological parameters. It is constructed by computing the spectral composition of light remitted from the skin given parameters specifying its structure and optical properties. The model is representative of all the normal human skin colours. irrespective of racial origin, age or gender. Abnormal skin colours do not conform to this model and thus can be detected. Once the model is constructed, for each pixel in a colour image its histological parameters are computed from the model. Represented as images, these 'parametric maps' show the concentration of dermal and epidermal melanin, blood and collagen thickness across the imaged skin as well as locations where abnormal colouration exists. In a clinical study the parametric maps were used by a clinician to detect the presence of malignant melanoma in a set of 348 pigmented lesions imaged using a commercial device, the SIAscope. Logistic regression identified the presence of melanin in the dermis, the abnormal distribution of blood within the lesion and the lesion size as the most diagnostically informative features. Classification based on these features showed 80.1% sensitivity and 82.7% specificity in melanoma detection. (C) 2003 Elsevier B.V. All rights reserved.
3D freehand ultrasound has extensive application for organ volume measurement and has been shown to have better reproducibility than estimates of volume made from 2D measurement followed by interpolation to 3D. One ke...
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ISBN:
(数字)9783540399032
ISBN:
(纸本)9783540204640
3D freehand ultrasound has extensive application for organ volume measurement and has been shown to have better reproducibility than estimates of volume made from 2D measurement followed by interpolation to 3D. One key advantage of free-hand ultrasound is that of image compounding, but this advantage is lost in many automated reconstruction systems. A novel method is presented for the automated segmentation and surface reconstruction of organs from sparse 3D ultrasound data. Preliminary results are demonstrated for simulated data, and two cases of in-vivo data;breast ultrasound and imaging of ovarian follicles.
Helical tomotherapy, a new approach for Intensity Modulated Radiation Therapy, employs a fan-beam of radiation from a source mounted in a CT-like ring gantry. Complex conformal dose delivery is achieved by modulating ...
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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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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, all of which involve design choices that are subject to debate. Moreover, most have numerical parameters whose value must be specified by the user. This paper proposes a principled method for evaluating design choices and choosing parameter values. This method can also be used to compare competing spatial normalization algorithms. We demonstrate the method through a performance analysis of a particular nonaffine deformation algorithm, ANIMAL.
A method to determine the number and location of seed images in clusters on a fluoroscopic image was developed based on a statistical analysis of cluster size in simulated fluoroscopic images. A modified 3-film techni...
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This paper deals with topology preservation in the framework of 3-D image registration using a multiscale parametric deformation field model. Using a B-spline deformation field yields a polynomial closed form expressi...
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For obtaining high-resolution reconstruction of the cerebral r vasculature, cone-beam projections in 3D computed rotational angiography (CRA) are acquired over a circular field of view (FOV) of 28 cm, resulting in a t...
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ISBN:
(数字)9783540399032
ISBN:
(纸本)9783540204640
For obtaining high-resolution reconstruction of the cerebral r vasculature, cone-beam projections in 3D computed rotational angiography (CRA) are acquired over a circular field of view (FOV) of 28 cm, resulting in a truncation of the data. This results in erroneous values of reconstruction within the region of interest that worsens laterally towards the periphery. In this paper, an application of linear prediction is explored for alleviating the effects of truncation in CRA, and its impact on image registration and also reprojection, an important tool in 3D visualization and image enhancement algorithms in CRA. New observations on the effects of taper in the extrapolated segment on filtered projections, and their implications on 3D reconstruction in CRA lead to windowed extrapolation. Results of the new algorithms on a mathematical phantom and real data are promising.
We describe a new approach to predicting the maximum target registration error for fiducial registration. The approach is based on the analysis of a spatial stiffness model that produces an analytic expression of the ...
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We describe a new approach to predicting the maximum target registration error for fiducial registration. The approach is based on the analysis of a spatial stiffness model that produces an analytic expression of the maximum error that is closely related to the formula reported in Fitzpatrick et al. The methodology we used was developed by Lin et al. The analytic stiffness results yield error estimates that closely match those from numerical simulations.
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.
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