Always MRI and CT Medical images are noisy so that preprocessing is necessary for enhance these images to assist clinicians and make accurate diagnosis. Firstly, in the proposed method uses two denoising filters (Median and Slantlet) are applied to images in parallel and the best enhanced image gained from both filters is voted by use PSNR and MSE as image quality measurements. Next, extraction of brain tumor from cleaned images is done by segmentation method based on k-mean.  The result shows that the proposed method is giving an optimal solution due to denoising method which is based on multiple filter types to obtain best clear images and that is leads to make the extraction of tumor more precision best.<

This study was conducted on Lake Hamrin situated in Diyala governorate, focal Iraq, between latitudes 44^º 53ʹ 26.16 '- 45º 07 ʹ 28.03^ʺ and 34º 04ʹ 24.75^ʺ ــ 34º 19ʹ 12.74^ʺ . As in this study, the surface area of Hamrin Lake was calculated from satellite images during the period from October 2019 to September 2020, with an average satellite image for each month, furthermore,by utilizing the Normalized Differences Water Index (NDWI), the largest surface area was 264,617 km² for October and the lowest surface area 140.202 km² for September. The surface temperature of the lake water was also calculated from satellite images of the Landsat 8 satellite, based on ban

A   snake   is   an   energy-minimizing   spline   guided   by   external

constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and motion tracking. We have used snakes successfully for segmentation, in  which  user-imposed  constraint forces guide the snake near features of interest (anatomical structures). Magnetic Resonance Image (MRI) data set and Ultrasound images are used for our experiments.

Medical imaging is a technique that has been used for diagnosis and treatment of a large number of diseases. Therefore it has become necessary to conduct a good image processing to extract the finest desired result and information. In this study, genetic algorithm (GA)-based clustering technique (K-means and Fuzzy C Means (FCM)) were used to segment thyroid Computed Tomography (CT) images to an extraction thyroid tumor. Traditional GA, K-means and FCM algorithms were applied separately on the original images and on the enhanced image with Anisotropic Diffusion Filter (ADF). The resulting cluster centers from K-means and FCM were used as the initial population in GA for the implementation of GAK-Mean and GAFCM. Jaccard index was used to s

The aim of this research is to determine the most important and main factors that lead to Preeclampsia. It is also about finding suitable solutions to eradicate these factors and avoid them in order to prevent getting Preeclampsia. To achieve this, a case study sample of (40) patients from Medical City - Oncology Teaching Hospital was used to collect data by a questionnaire which contained (17) reasons to be investigated. The statistical package (SPSS) was used to compare the results of the data analysis through two methods (Radial Bases Function Network) and (Factorial Analysis). Important results were obtained, the two methods determined the same factors that could represent the direct reason which causes Preecla

This research study Blur groups (Fuzzy Sets) which is the perception of the most modern in the application in various practical and theoretical areas and in various fields of life, was addressed to the fuzzy random variable whose value is not real, but the numbers Millbh because it expresses the mysterious phenomena or uncertain with measurements are not assertive. Fuzzy data were presented for binocular test and analysis of variance method of random Fuzzy variables , where this method depends on a number of assumptions, which is a problem that prevents the use of this method in the case of non-realized.

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Background: CA19-9 is among a group of mucin glycoprotein sialosyl lewis antigen (SLA) having come to be recognized as a circulating cancer associated antigen for gastrointestinal cancer. Basic research into cancer causation will be a powerful determinant of intervention in the transformation process reinforcing the need for developing effective molecular tumor biomarkers for early detection of malignant evolution in tissues of organs i.e. stomach, colon and rectum. In this study, we make a comparsion between the levels of CA19-9 in sera and tissues of patients with these diseases to see if their levels are proportional and if it is a tumor marker that affect with malignant cells in tissues of these org

Database is characterized as an arrangement of data that is sorted out and disseminated in a way that allows the client to get to the data being put away in a simple and more helpful way. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work studies the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on EEG Big-data as a case study. The proposed approach showed clear enhancement for managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG r

Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin