DYNAMIC KEY BLOCK DECISION WITH SPATIO-TEMPORAL ANALYSIS FOR WYNER-ZIV VIDEO CODING

ABSTRACT Wyner-Ziv coding has been recognized as the most popular method up to now. For traditional WZC, side information is generated from intra-coded frames for use in the decoding of WZ frames. The unit for intra-coding is a frame and the distance between key-frames is kept constant. In this paper, the unit for intra-coding is a block, and the temporal distance between two consecutive key blocks can varying with time. A block is assigned a mode (WZ or intra-coded), depending on the result of spatio-temporal analysis, and encoded in an alternative manner. This strategy improves the overall coding efficiency, while maintaining a low encoder complexity. The performance gain can achieve up to 6 dB with respect to the traditional pixel-domain WZC

Development and Validation of an Osteoporosis Self-Assessment Tool for Taiwan (OSTAi) Postmenopausal Women-A Sub-Study of the Taiwan OsteoPorosis Survey (TOPS)

<div><p>Background</p><p>To develop an OSTAi tool and compare this with the National Osteoporosis Foundation recommendations in 2013 (NOF 2013) for bone mineral density (BMD) testing among Taiwan postmenopausal women.</p><p>Methods</p><p>Taiwan Osteoporosis Association (TOA) conducted a nationwide BMD survey by a bus installed with a dual energy X-ray absorptiometry (DXA) between 2008 and 2011. All of the participants completed questionnaire, which included demographics and risk factors of osteoporotic fracture in FRAX tool. We used the database to analyze potential risk factors for osteoporosis and followed the model by Koh et al. to develop a risk index via multiple variable regression analysis and item reduction. We used the index values to set up a simple algorithm (namely OSTAi) to identify those who need BMD measurement. Receiver operating characteristic (ROC) curve and the area under the curve (AUC) was used to compare the sensitivity/specificity analysis of this model with that of recommendations by NOF 2013.</p><p>Results</p><p>A total of 12,175 Taiwan postmenopausal women enrolled in this survey. The index value was derived by age and body weight of the participants according to weighted odds of each risk factor and the selected cutoff value was set at “-1”. There are 6393 (52.5%) participants whose index value is below “-1” and whose risk of osteoporosis was 57.5% (3674/6393). The AUC for OSTAi and NOF 2013 were 0.739 (95% confidence interval (CI), 0.728–0.749, P<0.001) and 0.618 (95% CI, 0.606–0.630, P<0.001), respectively. The sensitivity and specificity of OSTAi, at the selected cutoff value of -1, and NOF 2013 to identify osteoporosis were 73.1%, 62.0% and 78.3%, 45.7%, respectively.</p><p>Conclusions</p><p>As OSTA for Asian populations, OSTAi is an useful tool to identify Taiwan postmenopausal women with osteoporosis, In comparison with NOF 2013, OSTAi may be an easier and better tool for referral to BMD measurement by DXA in this area.</p></div

Number of woman with T-score at -2.5 or below according to site.

<p>Number of woman with T-score at -2.5 or below according to site.</p

A plot of the OSTAi value versus the lowest T-score at any site.

<p>The horizontal line indicates a T-score of -2.5. The vertical lines mark the OSTAi value cutoffs of -1 and -4, which identified postmenopausal women at low (≥ -1), medium (-1 to -4), and high risk (≤-4) for osteoporosis.</p

Demographics of participants.

<p># Lowest T-score among lumbar spine, femoral neck and total hip</p><p>* Definition same as those of FRAX</p><p>@ n = answered yes in the questionnaire,</p><p>N = total number who answered the questionnaire</p><p>Demographics of participants.</p

Positive and negative predictive value of OSTAi index and NOF 2013.

<p>a, T-score ≤-2.5 at femoral neck, total hip, or lumbar spine;</p><p>b, Positive predictive value;</p><p>c, Sensitivity;</p><p>d, Negative predictive value;</p><p>e, Specificity</p><p>Positive and negative predictive value of OSTAi index and NOF 2013.</p

Regression coefficients for the univariate and multiple variable model.

<p>*SE: standard error</p><p>Regression coefficients for the univariate and multiple variable model.</p

The ROC curves for comparison.

<p>(a)The ROC curves for final model variables. ROC curves of two-variable model (age and weight), three-variable model, four-variable model, and total seven variables in predicting osteoporosis (b)The ROC curves for the OSTAi index and NOF 2013 recommendations.</p

Disposition of participants.

<p>Participants whose BMD was missing at any one site for any reason and participants with extreme values (deviating from the mean by more than three times the standard deviation) were excluded from data analysis.</p