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Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. Medical image segmentation refers to portioning the images into small, disjointed parts for simplifying the processes of analysis and examination. Rician and speckle noise are different types of noise in magnetic resonance imaging (MRI) that affect the accuracy of the segmentation process negatively. Therefore, image enhancement has a significant role in MRI segmentation. This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image. The proposed techniques consider the values of Peak Signal to Noise Ratio (PSNR) and the level of noise as inputs to the attention-U-Net model for segmentation of the tumor. The framework has been divided into three stages: removing speckle and Rician noise, the segmentation stage, and the feature extraction stage. The framework presents solutions for each problem at a different stage of the segmentation. In the first stage, the framework uses Vibrational Mode Decomposition (VMD) along with Block-matching and 3D filtering (Bm3D) algorithms to remove the Rician. Afterwards, the most significant Rician noise-free images are passed to the three different methods: Deep Residual Network (DeRNet), Dilated Convolution Auto-encoder Denoising Network (Di-Conv-AE-Net), andDenoising Generative Adversarial Network (DGAN-Net) for removing the speckle noise. VMDand Bm3D have achieved PSNR values for levels of noise (0, 0.25, 0.5, 0.75) for reducing the Rician noise by (35.243, 32.135, 28.214, 24.124) and (36.11, 31.212, 26.215, 24.123) respectively. The framework also achieved PSNR values for removing the speckle noise process for each level as follows: (34.146, 30.313, 28.125, 24.001), (33.112, 29.103, 27.110, 24.194), and (32.113, 28.017, 26.193, 23.121) forDeRNet, Di-Conv-AE-Net, and DGAN-Net, respectively. The experiments that have been conducted have proved the efficiency of the proposed framework against classical filters such as Bilateral, Frost, Kuan, and Lee according to different levels of noise. The attention gate U-Net achieved 94.66 and 95.03 in the segmentation of free noise images in dice and accuracy, respectively. [ABSTRACT FROM AUTHOR]

The success in the dental implant field is huge and still there are some dilemmas to overcome. Failure of dental implant is one of these dilemmas. Although, failure rate is very limited, however, it is concerning as most of the failure is because of infection, prosthetic part loosening or other less affecting reasons. This study aims to compare the level of RANKL, OPG and their relations to each other in two groups. One of these groups is completely integrated implant and the other was incompletely integrated implant. 65 implant patients were selected for this study according to world health organization. Periimplantsulcular fluid from the implant side wastaken using paper strip. All samples were tested using ELISA test to measure the level of opg and rankl in these samples. Results showed that opg was significantly higher than rankl in normally integrated implant and was lower than rankl in partially integrated implant. In addition, there is significant difference between normally integrated group and partially integrated group for both opg and rankl with highest mean for the partially integrated group. It can be concluded that opg was higher than rank in the normally integrated group and lower than rankl in the partially integrated group. Besides, there is a positive correlation between rankl and opg in both groups which mean that there may be other factors that can affect the opg-rankl levels and make one of them higher than the other. [ABSTRACT FROM AUTHOR]