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Gradient vector flow (GVF), an elegant external force for parametric deformable models, can capture object boundaries from both sides. A new geometric deformable model is proposed that combines GVF and the geodesic active contour model. The level set method is used as the numerical method of this model. The model is applied for echocardiographic image segmentation.
In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.
This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability.
To demonstrate the feasibility of a novel large-field digital mammography technique, a 1024 x 1024 pixel Loral charge-coupled device (CCD) focal plane array (FPA) was positioned in a mammographic field with one- and two-dimensional scan sequences to obtain 950 x 1800 pixel and 3600 x 3600 pixel composite images, respectively. These experiments verify that precise positioning of FPAs produced seamless composites and that the CCD mosaic concept has potential for high-resolution, large-field imaging. The proposed CCD mosaic concept resembles a checkerboard pattern with spacing left between the CCDs for the driver and readout electronics. To obtain a complete x-ray image, the mosaic must be repositioned four times, with an x-ray exposure at each position. To reduce the patient dose, a lead shield with appropriately patterned holes is placed between the x-ray source and the patient. The high-precision motorized translation stages and the fiber-coupled-scintillating-screen-CCD sensor assembly were placed in the position usually occupied by the film cassette. Because of the high mechanical precision, seamless composites were constructed from the subimages. This paper discusses the positioning, image alignment procedure, and composite image results. The paper only addresses the formation of a seamless composite image from subimages and will not consider the effects of the lead shield, multiple CCDs, or the speed of motion.
The ventrolateral portion of the intermediate reticular formation of the medulla (ventrolateral medulla, VLM), including the C1/A1 groups of catecholaminergic neurons, is thought to be involved in control of sympathetic cardiovascular outflow, cardiorespiratory interactions, and reflex control of vasopressin release. As all these functions are affected in patients with multiple systems atrophy (MSA) with autonomic failure, we sought to test the hypothesis that catecholaminergic (tyrosine hydroxylase [TH]-positive) neurons of the VLM are depleted in these patients. Medullas were obtained at autopsy from 4 patients with MSA with prominent autonomic failure and 5 patients with no neurological disease. Patients with MSA had laboratory evidence of severe adrenergic sudomotor and cardiovagal failure. Tissue was immersion fixed in 2% paraformaldehyde at 4 degrees C for 24 hours and cut into 1-cm blocks in the coronal plane from throughout the medulla. Serial 50-microm sections were collected and one section every 300 microm was stained for TH. There was a pronounced depletion of TH neurons in the rostral VLM in all cases of MSA. There was also significant reduction of TH neurons in the caudal VLM in 3 MSA patients compared with 3 control subjects. In 2 MSA cases and in 2 control subjects, the thoracic spinal cord was available for study. There was also depletion of TH fibers and sympathetic preganglionic neurons (SPNs) in the 2 MSA cases examined. Thus, depletion of catecholaminergic neurons in the VLM may provide a substrate for some of the autonomic and endocrine manifestations of MSA.
The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.