Titlebook: Information Processing in Medical Imaging; 15th International C James Duncan,Gene Gindi Conference proceedings 1997 Springer-Verlag Berlin

Exonerate

Acoustic and elastic imaging to model disease-induced changes in soft tissue structure,riations in the elastic properties of tissue can reveal disease mechanism, in vivo. On a larger scale, elasticity imaging describes the macroscopic elastic properties of soft tissues. We present an analysis framework and preliminary results for studying disease-induced changes in soft tissue using t

上漲

2D intravascular E.I.T. using a non-iterative, non-linear reconstruction algorithm,eries of 2D discretized images of the impedance distribution of the arterial wall..The 2D-IIC uses a differential measurement technique based on our early version of the IIC [1], but features two new elements: asymetrically placed electrodes and a rotational motion of the catheter around its longitu

isotope

Optimal data types in optical tomography,ty of the simultaneous reconstruction of absorption and diffusion images, by using both a simple circular case with embedded inhomogeneities, and a complex neonatal head model, and show that improvements are possible by combining suitable measurement types. We analyse the potential ability of the re

發(fā)起

,Using spatial-temporal images for analysis of gated cardiac tomographic data: The “M-Mode” revisitear function with perfusion/metabolism. Extracting indices of regional function from these 4D data sets (3D space + time) remains a challenge. All approaches currently used to quantitate gated tomographic sequences deal . with the spatial and temporal data. As functional information is based on close

craven

Automated segmentation of brain exterior in MR images driven by empirical procedures and anatomicaltensity threshold for segmenting the brain exterior is automatically determined by locating the choroid plexus. This is done by finding peaks in a series of histograms taken over regions specified using anatomical knowledge. Intensity inhomogeneities are accounted for by adjusting the global intensi

倒轉(zhuǎn)

Automated 3D segmentation using deformable models and fuzzy affinity,f the imaging modality used (e.g., MRI, CT, echocardiography). Our method is automatic and requires as initialization a single pixel/voxel within the boundaries of the object. Existing segmentation techniques either require much more information during initialization, such as an approximation to the

obstruct

Segmentation of medical image objects using deformable shape loci,e and expected shape variation across a population, and (2) methods for combining such shape models with unclassified image data to extract modeled objects. Described in this paper is such an approach to model-based image segmentation, called . (DSL), that has been successfully applied to 2D MR slic

AGATE

Malleable

Approximate distributions for Maximum Likelihood and maximum , estimates under a Gaussian noise mod or other lower bounds on variance. In order to better characterize the distribution of ML and MAP estimates under these conditions, we derive an approximate density for the conditional distribution of such estimates. In one example, this approximate distribution captures the essential features of t

Spinal-Tap