David E. Gustafson

Dynamic measurement of parameters within a sequence of images

A method of dynamically measuring parameters within a series of images using image processing is disclosed. A sequence of ultrasound images is generated by means of an ultrasound system. A user determines at least one region of interest within a first image. Then, at least one parameter for each region of interest is evaluated, e.g. the number of pixels exceeding a pre-defined intensity are counted. A new region of interest within a sequential image is searched within a search area around the predefined region of interest which best matches the region of interest. This is done for all images of a sequence whereby the new region of interest which best matches the region of interest of the previous image is used as a region of interest for the following image.

New ultrasound image display with extended field of view

The narrow fields of view obtained from real-time ultrasound transducers, especially with linear array transducers, allow focused evaluation of a specific site but often without any anatomic reference. To allow medical ultrasound imaging to be used in more diverse clinical settings, we have created a new acquisition and display process that allows extended field of view (XFOV) imaging. To produce an XFOV image, extended acoustic slices are obtained by maneuvering the transducer along the body surface or inside. As the images are acquired, they are correlated, aligned, and spliced together into a long composite view, all without the use of a position sensor. This computationally intensive process involves image registration, geometric image transformation, panoramic image construction, and image display. The XFOV process executes in real-time on our programmable ultrasound processing subsystem, the programmable ultrasound image processor, which fits within an existing ultrasound system and supports native ultrasound signal and image processing.

A Bayesian framework for noise covariance estimation using the facet model

In image processing literature, thus far, researchers have assumed the perturbation in the data to be white (or uncorrelated) having a covariance matrix /spl sigma//sup 2/ I, i.e., assumption of equal variance for all the data samples and that no correlation exists between the data samples. However, there have been very few attempts to estimate noise characteristics under the assumption that there is a correlation between data samples. In this work, we propose a new and a novel approach for the simultaneous Bayesian estimation of the unknown colored or correlated noise (population) covariance matrix and the hyperparameters of the covariance model using the well-known facet model. We also estimate the facet model coefficients. We use the facet model because of its simple, yet elegant, mathematical formulation. We use the generalized inverted Wishart density as the prior model for the noise covariance matrix. We place a structure on the covariance matrix using the parameters of a correlation filter. These hyperparameters are estimated by a new extension of the expectation-maximization algorithm called the generalized constrained expectation maximization algorithm that we developed.

Scalable ultrasound PACS: evolving needs for multimode ultrasound image and data management

We propose a scalable approach to ultrasound PACS. The general lack of any network interface capability on a large percentage of installed ultrasound scanners limits the solution available in the near term. A staged implementation beginning with a small number of ultrasound scanners interfaced to a single networked acquisition station is proposed. Initial mini-PACS may provide better utilization of the shared resources, such as archive and print servers and imagers, which would be cost prohibitive in a one-machine-per-scanner configuration. As the system requirements grow and ultrasound systems add direct network support, mini-PACS performances can overcome the initial single acquisition node bottleneck encountered with video-capture based systems, and ultrasound PACS can be integrated into a full hospital-wide PACS.

Optimal ridge orientation estimator using integrated second directional derivative

In this paper, we discuss a unified theory for and perfor- mance evaluation of the ridge direction estimation through the mini- mization of the integral of the second directional derivative of the gray-level intensity function. The primary emphasis of this paper is on the ridge orientation estimation. The subsequent ridge detection can be performed using the traditional methods of using the zero crossing of the first directional derivative. The performance evalua- tion of the ridge orientation estimation is performed in terms of the mean orientation bias and orientation standard deviation given the true orientation and the same two measures given the noise stan- dard deviation. We discuss two forms of our new ridge detector— first (ISDDRO-CN) using the noise covariance matrix estimation pro- cedure under colored noise assumption, and the second (ISDDRO- WN) using the white noise assumption. ISDDRO-CN performs better than the ISDDRO-WN in the presence of strong correlated noise. When the noise levels are moderate it performs as well as ISDDRO-WN. ISDDRO-CN has superior noise sensitivity character- istics. We also compare both forms of our algorithm with the algo- rithm, Maximum Level Set Extrinsic Curvature (MLSEC) designed by A. Lopez (IEEE Trans. Patter Anal. Mach. Intell. 21, 327-335 (1999)).