John S. Riley

Exploiting Walsh-based attributes to stereo vision

This paper presents a new stereo feature matching method that extracts the disparity measure for the recovery of depth information in 2-D stereo images. In this method, a stereo pair of images are transformed row for row into strings carrying spatially varying Walsh coefficients as attributes. The significance of the information carried by the Walsh coefficients is expressed mathematically and through experimental evaluations. The choice of the Walsh coefficients in contrast to other orthogonal transform coefficients is a direct result of their computational simplicity and their interpretative meaning in terms of the information contained in the spatial domain. The string-to-string matching technique used to bring the two strings into correspondence integrates, into a unified process, both the feature detection and the feature matching processes. The uniqueness and the ordering constraints are explicitly integrated into this string-to-string matching technique. Both the issues of Gaussian filtering and the importance of enforcing the epipolar line constraint are addressed in view of the application of the proposed method. Experimental results are given and assessed in terms of both the accuracy in stereo matching and the ensuing computational requirements.

Blood biomarkers of endocrine, immune, inflammatory, and metabolic systems in obstructive sleep apnea

OBJECTIVE/BACKGROUND Obstructive sleep apnea (OSA) is a common disorder, affecting over 100 million adults. Untreated OSA leads to serious health consequences and perturbations in endocrine, immune, inflammatory, and metabolic systems. Study objectives are to evaluate the association between OSA and biomarkers, and to test the hypothesis that a combination of markers may be useful in screening for OSA. PATIENTS/METHODS A multicenter trial was conducted enrolling symptomatic male patients with suspected OSA. All subjects underwent in-laboratory overnight polysomnography. A non-symptomatic control group was also obtained. Eleven biomarkers were tested: HbA1c, CRP, EPO, IL-6, uric acid, cortisol, hGH, prolactin, testosterone, DHEA (Beckman Coulter UniCel DxC 600i Synchron® Access® Clinical Systems), IGF-1. RESULTS 73 male subjects were enrolled; 26 had moderate/severe OSA. ROC curve analysis showed HbA1c, CRP, EPO, IL-6, and Uric Acid (AUCs: 0.76, 0.73, 0.65, 0.65, 0.61) were superior to the Epworth Sleepiness Scale (AUC: 0.52). Concurrent elevation of HbA1c and CRP provide even greater predictive power. A combination of elevated HbA1c, CRP, and EPO provided 0.08 increase in AUC (0.84 [0.75 - 0.94]) over individual markers (p<0.05), with high sensitivity (85%), and specificity (79%) for moderate/severe OSA. CONCLUSIONS OSA induces characteristic endocrine, immune, inflammatory, and metabolic disturbances that can be detected with blood biomarkers. These biomarkers are superior to standard screening questionnaires. Various clusters of these biomarkers have an even greater association with OSA and thus may represent physiologic signatures of the disorder that may have value in initial screening for OSA as well as for follow-up of therapy response.

Adaptive Filtering for Flow-Cytometric Particles

Abstract This paper studies the effects of FIR filtering and introducesan adaptive FIR filtering technique designed specificallyto smooth one and two dimensional accumulated flow-cytometric particles through frequency histograms. Theadaptive smoothing technique illustrated here will beshown to be particularly useful in compensating the unevenhistogram accumulation effects which take place whendata is accumulated at the lower histogram channels versusthe higher histogram channels. Linear smoothing techni-ques will not compensate for this phenomenon which isinherent to all histograms of accumulated data. In this view,a thorough analysis is provided to deal with the dilemmasimposed by the uneven accumulation of data within thesehistograms. 1 Introduction When cytometric data from specific blood cell particles isaccumulated into a frequency histogram with a finitenumber of channels (or bins) the accumulation processinherently smoothes the histogram since an averaging effectis taking place. When the cytometric data is accumulated inthe higher channels of the frequency histogram, the dis-tribution appears more spread and noisier. This is due to thefact that more channels are available and the inherentaveraging effect is diminished as data is accumulated over alarger band of channels. Because of this, when data isaccumulated in a histogram with a discrete number ofchannels, it gives rise to an uneven resolution within thehistogram.The idea behind the adaptive smoothing is to take advan-tage of the fact that as the filter coefficients are varied overan appropriate range, less smoothing will take place. Thefilter coefficients can be made to vary proportionally withrespect to the histogram channel where smoothing is takingplace. Smoothing is usually required prior to the analysis offrequency histogrammed data to attenuate the effect ofnoise. Smoothing through FIR ‘‘Finite Impulse Response’’filtering is by far the preferred method since it will not shiftthe position of the data distributions. A perspective ondensity estimators from histograms to several univariate andmultivariate statistical data analyses can be found in studies[1–3]. Other studies using histograms bring to focus severalissues including the notions of stability in the appearance ofthe histogram [4], the interpolations that can be achieved inkernel density estimators [5], as well as the practical buttraditional aspect of enhancing images through an adaptivehistogram equalization method [6].In general, it is desirable to select a smoothing function thathas the following criteria to ensure that the resulting data isnot distorted:1–The area under the curve should be maintained2–The mean of the distribution should be unchanged3–The change in the standard deviation should be kept to aminimumThese criteria for smoothing are maintained by traditionalFIR filtering provided that boundary conditions are satis-fied. A modified filtering scheme is introduced in this studybased on these mechanisms to customize the traditionalFIR-type filtering schemes for discrete time signals toaccommodate for data accumulated into frequency histo-grams. This modified filtering scheme is viewed as anadaptive filtering scheme that linearizes and accommodatesfor uneven histogram accumulation effects.To illustrate this problem a random Gaussian distributionwas created using the Box-Muller Method. The samepseudo-random distribution is illustrated with three separategain settings (1, 5, 10) as shown in Figure 1. When this datais histogrammed, as shown in Figure 2, the uneven accu-mulation problem is encountered and analyzed. Forexperimental evaluation, similar histogram accumulationproblems are discussed and analyzed utilizing Phycoery-thrin (PE) bead particles containing a variety of beadpopulations at various amounts of fluorochromes.

Use of blood biomarkers to screen for obstructive sleep apnea

Purpose Obstructive sleep apnea (OSA) is a highly prevalent disorder associated with increased risk for cardiovascular disease, diabetes, and other chronic conditions. Unfortunately, up to 90% of individuals with OSA remain without a diagnosis or therapy. We assess the relationship between OSA and blood biomarkers, and test the hypothesis that combinations of markers provide a characteristic OSA signature with diagnostic screening value. This validation study was conducted in an independent cohort in order to replicate findings from a prior feasibility study. Patients and methods This multicenter prospective study consecutively enrolled adult male subjects with clinically suspected OSA. All subjects underwent overnight sleep studies. An asymptomatic control group was also obtained. Five biomarkers were tested: glycated hemoglobin (HbA1c), C-reactive protein (CRP), uric acid, erythropoietin (EPO), and interleukin-6 (IL-6). Results The study enrolled 264 subjects. The combination of HbA1c+CRP+EPO (area under the curve 0.78) was superior to the Epworth Sleepiness Scale (ESS; 0.53) and STOP-Bang (0.70) questionnaires. In non-obese subjects, the combination of biomarkers (0.75) was superior to body mass index (BMI; 0.61). Sensitivity and specificity results, respectively, were: HbA1c+CRP+EPO (81% and 60%), ESS (78% and 19%), STOP-Bang (75% and 52%), BMI (81% and 56%), and BMI in non-obese patients (81% and 38%). Conclusion We verify our hypothesis and replicate our prior feasibility findings that OSA is associated with a characteristic signature cluster of biomarker changes in men. Concurrent elevations of HbA1c, CRP, and EPO levels should generate a high suspicion of OSA and may have utility as an OSA screening tool. Biomarker combinations correlate with OSA severity and, therefore, may assist sleep centers in identifying and triaging higher risk patients for sleep study diagnosis and treatment.