Showing papers in "Medical Image Analysis in 1996"

TL;DR: The rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching and motion tracking is reviewed.

TL;DR: In this paper, an information-theoretic approach for finding the registration of volumetric medical images of differing modalities is presented, which is achieved by adjustment of the relative position and orientation until the mutual information between the images is maximized.

TL;DR: The soft tissue correlation and mutual information measures were found to provide the most robust measures of misregistration, providing results comparable to or better than those from manual point-based registration for all but the most truncated image volumes.

TL;DR: A new model-based segmentation technique combining desirable properties of physical models, shape representation by Fourier parametrization, and modelling of natural shape variability is described, which achieves a uniform mapping between object surface and parameter space.

TL;DR: The method was able to characterize the 3-D shape and motion of the LV both locally and globally, in a clinically useful way, and extract quantitative differences between normal and abnormal hearts and visualize them in a way that is useful to physicians.

TL;DR: A new approach to analyse the deformation of the left ventricle of the heart based on a parametric model that gives a compact representation of a set of points in a 3-D image is presented.

TL;DR: This paper proposes to use fuzzy edgeness and ridgeness images, thus avoiding the need for segmentation and using more of the available information from the original images, and shows that such fuzzy images can be used to achieve accurate registration.

TL;DR: Initial results indicate that image analysis is far more robust when specific consideration is taken of the imaging process and the h int representation is used, and this ability to choose features that are robust to breast compression and variations in breast composition is used.

TL;DR: The prototype system has been tested during six neurosurgical operations with very good results and initial results from phantom experiments show an overall system accuracy of better than 0.9 mm for intra-operative localization of features defined in pre-operative images.