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Purpose To predict the development of glaucomatous visual field (VF) defects using Fourier-domain optical coherence tomography (FD-OCT) measurements at baseline visit. Design Multi-center longitudinal observational study. Glaucoma suspects and pre-perimetric glaucoma participants in the Advanced Imaging for Glaucoma Study. Methods The optic disc, the peripapillary retinal nerve fiber layer (NFL), and macular ganglion cell complex (GCC) were imaged with FD-OCT VF was assessed every 6 months. Conversion to perimetric glaucoma was defined by VF pattern standard deviation (PSD) or glaucoma hemifield test (GHT) outside normal limits on 3 consecutive tests. Hazard ratios were calculated with the Cox proportional hazard model. Predictive accuracy was measured by the area under the receiver-operating-characteristic curve (AUC). Results Of 513 eyes (309 participants), 55 eyes (46 participants) experienced VF conversion during 41 ± 23 months of follow-up. Significant (p<0.05, Cox regression) FD-OCT risk factors included all GCC, NFL, and disc variables, except for horizontal cup-to-disc ratio. GCC focal loss volume (FLV) was the best single predictor of conversion (AUC=0.753, p<0.001 for test against AUC = 0.5). Those with borderline or abnormal GCC-FLV had a 4-fold increase in conversion risk after 6 years (Kaplan-Meier). Optimal prediction of conversion was obtained using the glaucoma composite conversion index (GCCI) based on a multivariate Cox regression model that included GCC-FLV, inferior NFL quadrant thickness, age, and VF PSD. GCCI significantly improved predictive accuracy (AUC=0.783) over any single variable (p=0.04). Conclusions Reductions in NFL and GCC thickness can predict the development of glaucomatous VF loss in glaucoma suspects and pre-perimetric glaucoma patients.

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In this paper a local pattern descriptor in high order derivative space is proposed for face recognition. The proposed local directional gradient pattern (LDGP) is a 1D local micropattern computed by encoding the relationships between the higher order derivatives of the reference pixel in four distinct directions. The proposed descriptor identifies the relationship between the high order derivatives of the referenced pixel in four different directions to compute the micropattern which corresponds to the local feature. Proposed descriptor considerably reduces the length of the micropattern which consequently reduces the extraction time and matching time while maintaining the recognition rate. Results of the extensive experiments conducted on benchmark databases AT&T, Extended Yale B and CMU-PIE show that the proposed descriptor significantly reduces the extraction as well as matching time while the recognition rate is almost similar to the existing state of the art methods.

Image steganography is the art of hiding secret message in grayscale or color images. Easy detection of secret message for any state-of-art image steganography can break the stego system. To prevent the breakdown of the stego system data is embedded in the selected area of an image which reduces the probability of detection. Most of the existing adaptive image steganography techniques achieve low embedding capacity. In this paper a high capacity Predictive Edge Adaptive image steganography technique is proposed where selective area of cover image is predicted using Modified Median Edge Detector (MMED) predictor to embed the binary payload (data). The cover image used to embed the payload is a grayscale image.Experimental results show that the proposed scheme achieves better embedding capacity with minimum level of distortion and higher level of security. The proposed scheme is compared with the existing image steganography schemes. Results show that the proposed scheme achieves better embedding rate with lower level of distortion.

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