IEEE transactions on medical imaging
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IEEE Trans Med Imaging · Sep 2003
An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation.
An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images. The input images may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels. ⋯ The INU artifact is formulated as a multiplicative bias field affecting the true MR imaging signal. By modeling the log bias field as a stack of smoothing B-spline surfaces, with continuity enforced across slices, the computation of the 3-D bias field reduces to that of finding the B-spline coefficients, which can be obtained using a computationally efficient two-stage algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms.
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IEEE Trans Med Imaging · Sep 2003
Comparative StudyNoise reduction for magnetic resonance images via adaptive multiscale products thresholding.
Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. ⋯ In the multiscale products, edges can be effectively distinguished from noise. Thereafter, an adaptive threshold is calculated and imposed on the products, instead of on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme better suppresses noise and preserves edges than other wavelet-thresholding denoising methods.
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IEEE Trans Med Imaging · Sep 2003
Comparative StudyFurther analysis of interpolation effects in mutual information-based image registration.
This paper presents an analysis of the mutual information (MI) metric in rigid-body registration of two digital images, in particular, local fluctuations of the MI value due to interpolation. In contrast to existing work in this area, this paper starts with two hypothetical continuous images, based on which both sampling and interpolation effects are analyzed. This analysis indicates that an "ideal" interpolator may not be able to completely suppress the undesirable local minima of the MI metric if the sampling effect is not negligible. Several preprocessing methods are discussed for reducing the interpolation effects.
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IEEE Trans Med Imaging · Sep 2003
Comparative StudyMutual information-based CT-MR brain image registration using generalized partial volume joint histogram estimation.
Mutual information (MI)-based image registration has been found to be quite effective in many medical imaging applications. To determine the MI between two images, the joint histogram of the two images is required. In the literature, linear interpolation and partial volume interpolation (PVI) are often used while estimating the joint histogram for registration purposes. ⋯ It turns out that the PVI method is a special case of the GPVE procedure. We have implemented our algorithm on the clinically obtained brain computed tomography and magnetic resonance image data furnished by Vanderbilt University. Our experimental results show that, by properly choosing the kernel functions, the GPVE algorithm significantly reduces the interpolation-induced artifacts and, in cases that the artifacts clearly affect registration accuracy, the registration accuracy is improved.