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.<

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Arabic text categorization for pattern recognitions is challenging. We propose for the first time a novel holistic method based on clustering for classifying Arabic writer. The categorization is accomplished stage-wise. Firstly, these document images are sectioned into lines, words, and characters. Secondly, their structural and statistical features are obtained from sectioned portions. Thirdly, F-Measure is used to evaluate the performance of the extracted features and their combination in different linkage methods for each distance measures and different numbers of groups. Finally, experiments are conducted on the standard KHATT dataset of Arabic handwritten text comprised of varying samples from 1000 writers. The results in the generatio

RA Ali, LK Abood, Int J Sci Res, 2017 - Cited by 2

The purpose of the study is to identify the teaching techniques that mathematics' teachers use due to the Brain-based learning theory. The sample is composed of (90) teacher: (50) male, (40) female. The results have shown no significant differences between male and female responses' mean. Additionally, through the observation of author, he found a lack of using Brain-based learning techniques. Thus, the researcher recommend that it is necessary to involve teachers in remedial courses to enhance their ability to create a classroom that raise up brain-based learning skills.  

LK Abood, RA Ali, M Maliki, International Journal of Science and Research, 2015 - Cited by 2

conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership functions in the neighborhood of each pixel under consideration. The advantages of the method are that it is less
sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation. 

The aim of the current study is to in evaluate the role of SOD activity in the previously reported oxidative stress in our laboratory(1), in the patients with different brain tumors. SOD activity was assayed according to riboflavin/NBT method and its specific activity was calculated in patients with benign and malignant brain tumors and control. Moreover the specific activity was compared in these samples according to gender and the occurrence of disease.Non significant elevation (P > 0.05) in SOD specific activity was observed in tissue of malignant tumors in comparison to that of in benign brain tumors. While a highly significant decrease (P < 0.001) of the specific activity was found in sera of malignant patients group in comparison to t

In this paper, some commonly used hierarchical cluster techniques have been compared. A comparison was made between the agglomerative hierarchical clustering technique and the k-means technique, which includes the k-mean technique, the variant K-means technique, and the bisecting K-means, although the hierarchical cluster technique is considered to be one of the best clustering methods. It has a limited usage due to the time complexity. The results, which are calculated based on the analysis of the characteristics of the cluster algorithms and the nature of the data, showed that the bisecting K-means technique is the best compared to the rest of the other methods used.

Background: Skull secondary tumors are malignant bone tumors which are increasing in incidence.Objective: The objectives of this study were to present clinical features , asses the outcome of patients with secondary skull tumors ,characterize the MRI features, locations, and extent of secondary skull tumors to determine the frequency of the symptomatic disease.Type of the study: This is a prospective study.Methods: This is a prospective study from February 2000 to February 2008. The patients were selected from five neurosurgical centers and one oncology hospital in Baghdad/Iraq. The inclusion criteria were MRI study of the head(either as an initial radiological study or following head CT scan when secondary brain tumor is suspected , vis