This study aimed to evaluate the variability of lumen (LA) and wall area (WA) measurements obtained on two successive MDCT acquisitions using energy-driven contour estimation (EDCE) and full width at half maximum (FWHM) approaches. Both methods were applied to a database of segmental and subsegmental bronchi with LA > 4 mm(2) containing 42 bronchial segments of 10 successive slices that best matched on each acquisition. For both methods, the 95% confidence interval between repeated MDCT was between -1.59 and 1.5 mm(2) for LA, and -3.31 and 2.96 mm(2) for WA. The values of the coefficient of measurement variation (CV(10), i.e., percentage ratio of the standard deviation obtained from the 10 successive slices to their mean value) were strongly correlated between repeated MDCT data acquisitions (r > 0.72; p < 0.0001). Compared with FWHM, LA values obtained using EDCE were higher for LA < 15 mm(2), whereas WA values were lower for bronchi with WA < 13 mm(2); no systematic EDCE underestimation or overestimation was observed for thicker-walled bronchi. In conclusion, variability between CT examinations and assessment techniques may impair measurements. Therefore, new parameters such as CV(10) need to be investigated to study bronchial remodeling. Finally, EDCE and FWHM are not interchangeable in longitudinal studies.
Abstract-We propose a new non-intrusive (reference-free) objective measure of speech intelligibility that is inspired from previous works on image sharpness. We define the audio Sharpness Index (aSI) as the sensitivity of the spectrogram sparsity to the convolution of the signal with a white noise, and we calculate a closed-form formula of the aSI. Experiments with various speakers, noise and reverberation conditions show a high correlation between the aSI and the well-established Speech Transmission Index (STI), which is intrusive (full-reference). Additionally, the aSI can be used as an intelligibility or clarity criterion to drive sound enhancement algorithms. Experimental results on stereo mixtures of two sounds show that blind source separation based on aSI maximization performs well for speech and for music.
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