Kevin Manbeck

A Predictive Score Index for the Outcome of Associated Biofeedback and Vaginal Electrical Stimulation in the Treatment of Female Incontinence

A group of 64 women with stress incontinence alone (20), urgency incontinence (7) and mixed incontinence (37) were treated during 12 sessions, each 20 minutes long, during 6 weeks with combined alternating biofeedback and intravaginal electrical stimulation. Of the patients 21 had a complete recovery, 20 recovered sufficiently to avoid other forms of treatment and 23 failed to respond to the treatment. Thus, the overall success rate for this treatment was 64%. Various physiological parameters were collected from each patient before the start of the treatment sessions. Patient age, estrogen status, detrusor hyperreflexia, intravaginal pressure, percent transmission of the abdominal pressure to the urethra, degree of intrinsic sphincter deficiency and compliance with therapy were significant factors affecting the success of treatment. A statistical analysis was performed on these measurements to generate a score index model capable of predicting the outcome of a treatment consisting of associated biofeedback and electrical stimulation. We present a reliable method for distinguishing between patients who will and will not respond to this form of treatment. The most significant variables predictive of a good reduction outcome are patient age, presence of estrogen, absence of detrusor instability and intrinsic sphincter deficiency, low urethral hypermobility and, most of all, compliance with treatment.

A Stochastic Shape and Color Model for Defect Detection in Potatoes

Abstract Automatic defect detection in color images of potatoes is complicated by the random variability ordinarily observed in a collection of normal potatoes. Stochastic models are developed that explicitly describe the random nature of the shape and color texture observed in normal potatoes. Shape and color simulations based on these models are realistic. Statistical tests based on the stochastic shape and color models are developed. The tests are capable of classifying an unknown object as either “acceptable potato” or “unacceptable potato.” Twelve potatoes are analyzed, and the experimental results are presented.

A new method for structure recognition in unsubtracted digital angiograms

Existing methods for automatically finding arteries in coronary angiograms rely on preprocessing. The authors develop a structure recognition system which is not sensitive to variations in image quality. This system utilizes a probabilistic contextual segmentation technique which performs image segmentation without preprocessing. This approach, the deformable template matcher, combines prior knowledge of the arterial tree, encoded as mathematical templates, with a stochastic deformation process described by a hidden Markov model. An introduction to the technique is presented along with recent enhancements of the structure recognition system.<<ETX>>

Deformable template models for emission tomography

The reconstruction of emission tomography data is an ill-posed inverse problem and, as such, requires some form of regularization. Previous efforts to regularize the restoration process have incorporated rather general assumptions about the isotope distribution within a patients body. A theoretical and algorithmic framework is presented in which the notion of a deformable template can be used to identify and quantify brain tumors in pediatric patients. Patient data and computer simulation experiments are presented which illustrate the performance of the deformable template approach to single photon emission computed tomography.

CT reconstruction of x-ray source profile of a medical accelerator

A technique is developed to measure the x-ray source intensity profile of a medical accelerator using its built-in collimators, and two reconstruction techniques are investigated.