In this paper optimal filters for the detection of coronary arteries with a diameter range of 0.5-6.0 mm in digital X-ray images are derived using a computational approach. This approach is based on the two requirements for optimal detection. First, the filter should maximize the number of detected true edges and minimize the number of detected false edges. Second, if an edge has been detected, its position should be as close as possible to the true edge position in the image. Since the grey value profile in a digital X-ray image associated with an arterial vessel is asymmetric, the theory on edge detection derived by Canny has been expanded with two additional boundary constraints to make it suitable for the derivation of filters for asymmetric edges. It is demonstrated that it is possible to derive optimal filters for coronary segments. The localization error, defined by the square root of the sum of the squared systematic and random errors in the assessment of the arterial diameter, depends on the size of the coronary artery and the amount of noise in the image. In this paper, an evaluation study is described to assess the relationship between localization error and the amount of noise upon the vessel profile. For that purpose, an analytical description of the vessel profile in an angiographic image was derived. For the larger arteries the relation between noise and localization error was found to be linear and no systematic over- or underestimations were observed, even if the noise level was very high. However, it can be shown that the smallest diameter that can be measured depends on the amount of noise present in the data. Even for images that contain only a low amount of noise, arterial diameters below 0.7 mm cannot be measured accurately. If the noise in the image increases, the lowest measurable arterial diameter value also increases. Also the random error increases rapidly for vessel diameters below 1.2 mm, but with a limited amount of noise and a diameter value above 0.7 mm the random error is still acceptable [0.15 mm (21%) for 0.7-mm vessels, 0.06 mm (6%) for 1-mm vessels].

The paper describes a method for the automatic location of facial features, such as eyes, nose and mouth, in image sequences using a neural network approach. It is shown that by modeling the feature sought as a structural assembly of micro-features, and by using a probabilistic interpretation of neural network outputs, it is possible to construct a location system that is more robust than a location system which uses the feature as a single entity. With this micro-feature approach, not only can the position of the features be found but shape of the features can be obtained as well.

Foreshortening of vessel segments in angiographic (biplane) projection images may cause misinterpretation of the extent and degree of coronary artery disease. The views in which the object of interest are visualized with minimum foreshortening are called optimal views. The authors present a complete approach to obtain such views with computer-assisted techniques. The object of interest is first visualized in two arbitrary views. Two landmarks of the object are manually defined in the two projection images. With complete information of the projection geometry, the vector representation of the object in the three-dimensional space is computed. This vector is perpendicular to a plane in which the views are called optimal. The user has one degree of freedom to define a set of optimal biplane views. The angle between the central beams of the imaging systems can be chosen freely. The computation of the orientation of the object and of corresponding optimal biplane views have been evaluated with a simple hardware phantom. The mean and the standard deviation of the overall errors in the calculation of the optimal angulation angles were 1.8 degrees and 1.3 degrees , respectively, when the user defined a rotation angle.

We present a system in which the global motion (3D rotation and translation) and the local motion (facial expressions) of the face of a talking person are estimated automatically from an input image sequence. First, the shape of the facial features, such as eyes and mouth, are robustly extracted from the images. Then, based on the extracted shape of the facial features, the global motion and facial expressions are estimated. No human assistance is necessary throughout the process. The system relies on the use of a priori knowledge about the scene and facial motions. In the feature extraction scheme this a priori knowledge is modeled by a descriptive tree of the scene and geometric shape representations of the facial features. The global motion can be easily obtained from these facial features. In the local motion estimation scheme the a priori knowledge is modeled in a belief network, in which knowledge about muscle actuators is represented, e.g., the interactions between the muscle actuators and their visible manifestations. A few experiments are included to illustrate the system.

Describes a new training method, the DRBP-algorithm, for sigmoid-function based multilayer networks. The key step in DRBP is the dynamical selection and autonomous control of the learning rate. Various experiments have shown that the DRBP-algorithm has achieved its goal of fast speed, secure stability and easy parameter selection in practice.>

As part of the development directed at the automated reporting of the location and degree of stenoses in a coronary arterial tree, the authors address the issue of automated labelling of coronary segments. With a priori information of the coronary anatomy related to the projection geometry, the branches of a coronary tree are labelled by a consistent inexact graph matching (CIGM) technique. They describe the CIGM technique, the cost functions and associated parameters, a representation of the segments of the arterial tree and their experience in labelling arteries in routinely acquired arteriograms.>

A real-time pipelined image processing system operating in a time division multiplexing mode to serve up to 16 cameras, was realized to count the mass of a flow of bottles on a conveyor belt. The realized mass counting system proved to be a powerful tool capable of continuously counting bottles with a speed of approximately 500000 bottles a day per measurement point and with an accuracy of less then 0.5%.>

Presents an objective and quantitative study of several thresholding techniques. For this study, three sets of test images to simulate various real situations have been designed. The accuracy of object feature measurement is used to assess the performance. The authors not only provide an insight into these techniques but also show the usefulness of synthetic images and ultimate measurement accuracy in segmentation evaluation and comparison.>

The contours of isolated objects in noisy images may be detected with a minimal cost contour detection algorithm. An algorithm that is based on the policy-iteration method for locating the closed minimal cost path is introduced. Computational results indicate that it is computationally more efficient than the dynamic programming approach. The method is applied to left ventricular contours in scintigraphic images, although it is applicable to any domain where a closed minimal cost path is to be computed in a matrix of cost coefficients.>

The usefulness of the fractal Brownian model in combination with pyramidal data structures is discussed. The model enables one to optimize the initialization of the texture pyramid. The ideas which are applied during the initialization phase are also applied during the segmentation. The segmentation algorithm is based on the complete linked pyramid and does not require prior knowledge about the number of segments. The segmentation result is obtained by walking along the links, guided by an error measure. The method does not require any predefined thresholds. All experimental PC program has been developed for the 1-D case.>

In this paper, we present the general methodology for extending different 2-D thresholding techniques to meet 3-D requirements. Several typical thresholding techniques in their 3-D forms have been implemented. The extended techniques have been tested using 3-D synthetic images and the results will be evaluated. Problems related to the extension and improvements made in the implementation will also be discussed.

Recursive Kalman filters are often used for noise reduction in image data. These linear filters are based on the second-order statistics of image and noise. The noise is effectively reduced by the filtering operation, but the edges in the image are blurred and image contrast is reduced as well. These effects decrease the subjective quality of the image. A simple and computationally fast scan-ordered one-dimensional Kalman filter is derived, which is then provided with additional structural information about the edges in the noisy image. This filter behaves like the original noise-smoothing Kalman filter if no edges are present but has a greatly improved step response. In this way the edge-blurring phenomenon is effectively reduced. Results of several experiments are presented to demonstrate the feasibility of our approach.