Mean shift based gradient vector flow for image segmentation

In recent years, gradient vector flow (GVF) based algorithms have been successfully used to segment a variety of 2-D and 3-D imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods may lead to biased segmentation results. In this paper, we propose MSGVF, a mean shift based GVF segmentation algorithm that can successfully locate the correct borders. MSGVF is developed so that when the contour reaches equilibrium, the various forces resulting from the different energy terms are balanced. In addition, the smoothness constraint of image pixels is kept so that over- or under-segmentation can be reduced. Experimental results on publicly accessible datasets of dermoscopic and optic disc images demonstrate that the proposed method effectively detects the borders of the objects of interest.

Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm

Image segmentation plays an important role in the analysis of retinal images as the extraction of the optic disk provides important cues for accurate diagnosis of various retinopathic diseases. In recent years, gradient vector flow (GVF) based algorithms have been used successfully to successfully segment a variety of medical imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods can lead to less accurate segmentation results in certain cases. In this paper, we propose the use of a new mean shift-based GVF segmentation algorithm that drives the internal/external energies towards the correct direction. The proposed method incorporates a mean shift operation within the standard GVF cost function to arrive at a more accurate segmentation. Experimental results on a large dataset of retinal images demonstrate that the presented method optimally detects the border of the optic disc.

Modelling the fresh properties of self-compacting concrete using support vector machine approach

The main objective of the study presented in this paper was to investigate the feasibility using support vector machines (SVM) for the prediction of the fresh properties of self-compacting concrete. The radial basis function (RBF) and polynomial kernels were used to predict these properties as a function of the content of mix components. The fresh properties were assessed with the slump flow, T50, T60, V-funnel time, Orimet time, and blocking ratio (L-box). The retention of these tests was also measured at 30 and 60 min after adding the first water. The water dosage varied from 188 to 208 L/m3, the dosage of superplasticiser (SP) from 3.8 to 5.8 kg/m3, and the volume of coarse aggregates from 220 to 360 L/m3. In total, twenty mixes were used to measure the fresh state properties with different mixture compositions. RBF kernel was more accurate compared to polynomial kernel based support vector machines with a root mean square error (RMSE) of 26.9 (correlation coefficient of R2 = 0.974) for slump flow prediction, a RMSE of 0.55 (R2 = 0.910) for T50 (s) prediction, a RMSE of 1.71 (R2 = 0.812) for T60 (s) prediction, a RMSE of 0.1517 (R2 = 0.990) for V-funnel time prediction, a RMSE of 3.99 (R2 = 0.976) for Orimet time prediction, and a RMSE of 0.042 (R2 = 0.988) for L-box ratio prediction, respectively. A sensitivity analysis was performed to evaluate the effects of the dosage of cement and limestone powder, the water content, the volumes of coarse aggregate and sand, the dosage of SP and the testing time on the predicted test responses. The analysis indicates that the proposed SVM RBF model can gain a high precision, which provides an alternative method for predicting the fresh properties of SCC.

Outgrown asthma does not mean no airways inflammation

Although some asthmatic children seem to recover from their asthma, 30–80% develop asthma again in later life. The underlying risk factors are unknown. The hypothesis for this study was that children with apparently outgrown asthma would have underlying airway inflammation. Nonbronchoscopic bronchoalveolar lavage was performed on normal children (n=35) and children who had wheezed previously (n=35). Eosinophils were raised in the lavage fluid of atopic children who had apparently outgrown asthma (median (interquartile range) 0.36 (0.05–0.74) compared to controls 0.10 (0–0.18), p=0.002). There was no relationship between length of remission and degree of airways eosinophilia. Thus, there is persistent airways inflammation in some children with outgrown asthma and this may be a risk factor for future relapse.

Propagating waves and magnetohydrodynamic mode coupling in the quiet-sun network

High-cadence multiwavelength optical observations were taken with the Dunn Solar Telescope at the National Solar Observatory, Sacramento Peak, accompanied by Advanced Stokes Polarimeter vector magnetograms. A total of 11 network bright points (NBPs) have been studied at different atmospheric heights using images taken in wave bands centered on Mg I b(1) - 0.4 Angstrom, Halpha, and Ca II K-3. Wavelet analysis was used to study wave packets and identify traveling magnetohydrodynamic waves. Wave speeds were estimated through the temporal cross-correlation of signals, in selected frequency bands of wavelet power, in each wavelength. Four mode-coupling cases were identified, one in each of four of the NBPs, and the variation of the associated Fourier power with height was studied. Three of the detected mode-coupling, transverse-mode frequencies were observed in the 1.2-1.6 mHz range (mean NBP apparent flux density magnitudes over 99-111 Mx cm(-2)), with the final case showing 2.0-2.2 mHz (with 142 Mx cm(-2)). Following this, longitudinal-mode frequencies were detected in the range 2.6-3.2 mHz for three of our cases, with 3.9-4.1 mHz for the remaining case. After mode coupling, two cases displayed a decrease in longitudinal-mode Fourier power in the higher chromosphere.