IEEE transactions on medical imaging
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We have developed a method to study the statistical properties of the noise found in various medical images. The method is specifically designed for types of noise with uncorrelated fluctuations. Such signal fluctuations generally originate in the physical processes of imaging rather than in the tissue textures. ⋯ However, statistical analysis suggests that the noise variance could be better modeled by a nonlinear function of the image intensity depending on external parameters related to the image acquisition protocol. We present a method to extract the relationship between an image intensity and the noise variance and to evaluate the corresponding parameters. The method was applied successfully to magnetic resonance images with different acquisition sequences and to several types of X-ray images.
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IEEE Trans Med Imaging · Oct 2004
Comparative Study Clinical TrialDetermining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels.
Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) defined on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. ⋯ Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.
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IEEE Trans Med Imaging · Oct 2004
Letter Comparative StudyA modeling approach for burn scar assessment using natural features and elastic property.
A modeling approach is presented for quantitative burn scar assessment. Emphases are given to: 1) constructing a finite-element model from natural image features with an adaptive mesh and 2) quantifying the Young's modulus of scars using the finite-element model and regularization method. A set of natural point features is extracted from the images of burn patients. ⋯ A three-dimensional finite-element model is built on top of the mesh with the aid of range images providing the depth information. The Young's modulus of scars is quantified with a simplified regularization functional, assuming that the knowledge of the scar's geometry is available. The consistency between the relative elasticity index and the physician's rating based on the Vancouver scale (a relative scale used to rate burn scars) indicates that the proposed modeling approach has high potential for image-based quantitative burn scar assessment.
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IEEE Trans Med Imaging · Oct 2004
Comparative StudyAtlas-based segmentation of pathological MR brain images using a model of lesion growth.
We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. ⋯ The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.
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IEEE Trans Med Imaging · Oct 2004
Comparative Study Clinical TrialAnalysis of 3-D myocardial motion in tagged MR images using nonrigid image registration.
Tagged magnetic resonance imaging (MRI) is unique in its ability to noninvasively image the motion and deformation of the heart in vivo, but one of the fundamental reasons limiting its use in the clinical environment is the absence of automated tools to derive clinically useful information from tagged MR images. In this paper, we present a novel and fully automated technique based on nonrigid image registration using multilevel free-form deformations (MFFDs) for the analysis of myocardial motion using tagged MRI. ⋯ We use both short-axis and long-axis images of the heart to estimate the full four-dimensional motion field within the myocardium. We also present validation results from data acquired from twelve volunteers.