Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.

А Novel AP Placement Algorithm Based on User Distribution for Indoor WLAN System

AP deployment is significant for indoor WLAN system to achieve seamless coverage. The available algorithms do not take user distribution into consideration so that poor user coverage and imbalanced network load occur. Therefore, this paper proposed a novel AP placement algorithm to bridge the AP deployment with user distribution. The proposed algorithm employs statistics theory to model the user distribution as its location and probability. Then we obtain the AP location based on the fuzzy C-clustering algorithm. The proposed algorithm is practical for implementation, which means the actual signal transmission isn't required in our proposed method. The simulation results show that the proposed algorithm could automatically achieve a good AP deployment with different user distribution, and provide a good performance in the maximum users and AP load balance in WLAN.

Computational Characterization of Reactive Intermediates of Carbon Monoxide Dehydrogenase

The catalytic oxidation of CO to CO2 by carbon monoxide dehydrogenases has been explored theoretically, and a large C-cluster model including the metal core [Ni-4Fe-4S] and surrounding residues and crystal water molecules was used in density functional calculations. The key species involved in the oxidation of CO at the C-cluster, Cred1, Cred2 and Cint, have been elucidated. On the basis of computational results, the plausible enzymatic mechanism for the CO oxidation was proposed. In the catalytic reaction, the first proton abstraction from the Fe（1）-bound water leads to a precursor to accommodate CO binding and the subsequently consecutive proton transfers from the metal-bound carboxylate to the amino acid residues facilitate the release of CO2. The hydrogen-bond network around the C-cluster formed by conserved residues His93, His96, Glu299, Lys563, and four water molecules in the active domain plays an important role in proton transfer and intermediate stabilization. Predicted geometries of key species show good agreement with the reported crystal structures.