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This paper presents a near-automatic process for separating vessels from background and other clutter as well as for separating arteries and veins in contrast-enhanced magnetic resonance angiographic (CE-MRA) image data, and an optimal method for three-dimensional visualization of vascular structures. The separation process utilizes fuzzy connected object(More)
A statistical description of X-ray CT (computerized tomography) imaging, from the projection data to the reconstructed image, is presented. The Gaussianity of the pixel image generated by the convolution (image reconstruction) algorithm is justified. The conditions for two pixel images to be statistically independent (for a given probability) and the(More)
For pt.I, see ibid., vol.11, no.1, p.53.61 (1992). Based on the statistical properties of X-ray CT imaging given in pt.I, an unsupervised stochastic model-based image segmentation technique for X-ray CT images is presented. This technique utilizes the finite normal mixture distribution and the underlying Gaussian random field (GRF) as the stochastic image(More)
In this paper, a new framework for stochastic MR image modeling is presented based on the local randomization, and a new model-based MR image analysis technique is developed in terms of stochastic regularization. By discussing the statistical properties of both pixel image and context image, an inhomogeneous hidden Markov random field model is proposed and(More)
Magnetic resonance angiography (MRA) is established as an important complementary technique to conventional angiography, and contrast–enhanced MRA (CE-MRA) offers even higher contrast between the vascular lumen and surrounding structures. MS-325 is a gadolinium-based MR contrast agent designed specifically for blood-pool imaging, or MRA, and is the only(More)
Finite Normal Mixture (FNM) model-based image segmentation techniques adopt the following detection-estimation-classification paradigm: 1) detect the number of image regions by using theoretical information criteria; 2) estimate model parameters by using expectation-maximization (EM)/classification-maximization (CM) algorithms; and 3) classify pixels into(More)
Our earlier study developed a computerized method, based on fuzzy connected object delineation principles and algorithms, for artery and vein separation in contrast enhanced Magnetic Resonance Angiography (CE-MRA) images. This paper reports its current development-a software package-for routine clinical use. The software package, termed 3DVIEWNIX-AVS,(More)
This paper presents a new approach for medical image analysis. It translates the object region-detection problem into a sensor array processing framework and detects the number of object regions based on the signal eigenstructure of the converted array system. The theoretical and experimental results obtained by using this approach on various medical images(More)