Optical Coherence Tomography (OCT) images are used to reveal retinal diseases and abnormalities, such as Diabetic Macular Edema (DME) and Age-related Macular Degeneration (AMD). Fluid regions are the main sign of AMD and DME and automatic Fluid Segmentation models are very helpful for diagnosis, treatment, and follow-up. This paper presents a two-path Neutrosophic (NS) Fully Convolutional Networks, referred as TPNFCN, as a fully-automated model for Fluid Segmentation. For this task, OCT images are first transferred to NS domain and then Inner Limiting Membrane (ILM) and Retinal Pigmentation Epithelium (RPE) layers as first and last layers of retina are segmented by graph shortest path algorithms in NS domain, respectively. Afterwards, a basic block of FCN is presented for Fluid Segmentation and this block is used in the architecture of the proposed TPNFCN. Both the basic block and TPNFCN are evaluated on 600 OCT scans of 24 AMD subjects containing different Fluid types. Results reveal that the proposed basic block and TPNFCN outperform five competitive models by improvement of 6.28%, 4.44% and 2.54% with respect to sensitivity, dice coefficients, and precision, respectively. It is also demonstrated that the proposed TPNFCN is robust against low number of training samples in comparison with current models.

Introduction: Use of an adhesive with a suitable composite resin is an important factor in tooth-colored restorations and increasing marginal sealing ability. The aim of this study was to evaluate the microleakage of various adhesives with composite resins using Fluid filtration and dye extraction techniques and assessment of the correlation between these two techniques.Materials and Methods: In this in vitro study, class II cavities were prepared on the proximal surfaces of 48 extracted sound human premolar teeth, measuring 1.5±0.5 mm in gingival width, 4 mm in occluso-gingival height and 1.3 of the inter-cuspal distance in width. The samples were divided into four groups (n=12). Group 1; OptiBond Solo Plus adhesive/Herculite XRV composite resin; Group 2: OptiBond Solo Plus adhesive/Master Dent composite resin; Group 3: Prime & Bond NT adhesive/Herculite XRV composite resin; Group 4: Prime & Bond NT adhesive/Master Dent composite resin. Microleakage was assessed primarily by Fluid filtration and then by dye extraction techniques. Data were analyzed with Kruskal-Wallis and Mann-Whitney tests, with Bonferroni correction and Spearman's rho correlation test at 95% confidence interval.Results: The minimum and maximum mean microleakage values were recorded in Fluid filtration and dye extraction techniques groups 1 and 4, respectively. There were significant differences between the experimental groups in microleakage with the two methods of leakage assessment (p value=0.001). Spearman's rho revealed a strongly direct correlation between the two methods (r=0.797, p value=0.000).Conclusion: In both methods of microleakage assessment, use of ethanol-based in comparison to acetone-based adhesive resulted in microleakage reduction. Application of light-cured composite resin contrary to self-cured composite resin with both types of adhesives increased marginal sealing ability.

Image Segmentation is a fundamental approach in the field of image processing and based on user’ sapplication. This paper propose an original and simple Segmentation strategy based on the EM approach thatresolves many informatics problems about hyperspectral images which are observed by airborne sensors. In afirst step, to simplify the input color textured image into a color image without texture. The final Segmentationis simply achieved by a spatially color Segmentation using feature vector with the set of color valuescontained around the pixel to be classified with some mathematical equations. The spatial constraint allowstaking into account the inherent spatial relationships of any image and its color. This approach provideseffective PSNR for the segmented image. These results have the better performance as the segmented imagesare compared with Watershed & Region Growing Algorithm and provide effective Segmentation for theSpectral Images & Medical Images.