Classification and Segmentation of MRI Brain Images using Support Vector Machine and Fuzzy C-means Clustering

An early diagnosis of brain disorders is very important for timely treatment of such diseases.Several imaging modalities are used to capture the anomalities by obtaining either the  physiological or morphological information. The scans obtained using imaging modalities such as magnetic resonance imaging (MRI) are investigated by the radiologists in order to diagnose the diseases. However such investigations are time consuming and might involve errors. In this paper, a fuzzy c-means clustering method is used for brain MRI image segmentation.The GLCM features are obtained from the segmented images and are subsequently mapped in to a PCA space. A support vector machine (SVM) classifier is used to classify brain MRI images taken from BRATS-13 images. The method is evaluated by employing various performance measures such as  Jaccard index, Dice index, mean square error (MSE), peak signal to noise ratio (PSNR). The results show that the method outperforms the existing methods

Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network

Technology and the rapid growth in the area of brain imaging technologies have forever made for a pivotal role in analyzing and focusing the new views of brain anatomy and functions. The mechanism of image processing has widespread usage in the area of medical science for improving the early detection and treatment phases. Deep neural networks (DNN), till date, have demonstrated wonderful performance in classification and segmentation task. Carrying this idea into consideration, in this paper, a technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with the image decomposition property of wavelet transform is proposed. The combination of both has a tremendous effect on sinking the size of the feature set for enduring further classification task by using DNN. A brain image dataset was taken and the proposed DWA-DNN image classifier was considered. The performance criterion for the DWA-DNN classifier was compared with other existing classifiers such as autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods

Novel analysis–forecast system based on multi-objective optimization for air quality index

© 2018 Elsevier Ltd The air quality index (AQI) is an important indicator of air quality. Owing to the randomness and non-stationarity inherent in AQI, it is still a challenging task to establish a reasonable analysis–forecast system for AQI. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results. In this study, a novel analysis–forecast system is proposed that consists of complexity analysis, data preprocessing, and optimize–forecast modules and addresses the problems of air quality monitoring. The proposed system performs a complexity analysis of the original series based on sample entropy and data preprocessing using a novel feature selection model that integrates a decomposition technique and an optimization algorithm for removing noise and selecting the optimal input structure, and then forecasts hourly AQI series by utilizing a modified least squares support vector machine optimized by a multi-objective multi-verse optimization algorithm. Experiments based on datasets from eight major cities in China demonstrated that the proposed system can simultaneously obtain high accuracy and strong stability and is thus efficient and reliable for air quality monitoring

Artificial Intelligence: Development and Applications in Neurosurgery

The last decade has witnessed a significant increase in the relevance of artificial intelligence (AI) in neuroscience. Gaining notoriety from its potential to revolutionize medical decision making, data analytics, and clinical workflows, AI is poised to be increasingly implemented into neurosurgical practice. However, certain considerations pose significant challenges to its immediate and widespread implementation. Hence, this chapter will explore current developments in AI as it pertains to the field of clinical neuroscience, with a primary focus on neurosurgery. Additionally included is a brief discussion of important economic and ethical considerations related to the feasibility and implementation of AI-based technologies in neurosciences, including future horizons such as the operational integrations of human and non-human capabilities

Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation

International audienceThis article describes a new clustering method for segmentation of Magnetic resonance imaging (MRI) brain images. Currently, when fuzzy clustering is applied to brain image segmentation, there are two main problems to be solved which are: (i) the sensitivity to noise and intensity non-uniformity (INU) artifact; (ii) the trapping into local minima and dependency on initial clustering centroids. For the purpose of obtaining satisfactory segmentation performance and dealing with the problems mentioned above, an effective method is developed within the scope of this paper. Firstly, a new objective function utilizing kernelized fuzzy entropy clustering with local spatial information and bias correction (KFECSB) is designed. We then propose a new algorithm based on an improved particle swarm optimization (PSO) with the new fitness function to better segment MRI brain images. To test its performance, the proposed algorithm has been evaluated on several benchmark images including the simulated MRI brain images from the McConnell Brain Imaging Center (BrainWeb) and the real MRI brain images from the Internet Brain Segmentation Repository (IBSR). In addition, a systematic comparison of the proposed algorithm versus five other state of the art techniques is presented. Experimental results show that the proposed algorithm can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provide better results than its competitors

Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation

International audienceThis article describes a new clustering method for segmentation of Magnetic resonance imaging (MRI) brain images. Currently, when fuzzy clustering is applied to brain image segmentation, there are two main problems to be solved which are: (i) the sensitivity to noise and intensity non-uniformity (INU) artifact; (ii) the trapping into local minima and dependency on initial clustering centroids. For the purpose of obtaining satisfactory segmentation performance and dealing with the problems mentioned above, an effective method is developed within the scope of this paper. Firstly, a new objective function utilizing kernelized fuzzy entropy clustering with local spatial information and bias correction (KFECSB) is designed. We then propose a new algorithm based on an improved particle swarm optimization (PSO) with the new fitness function to better segment MRI brain images. To test its performance, the proposed algorithm has been evaluated on several benchmark images including the simulated MRI brain images from the McConnell Brain Imaging Center (BrainWeb) and the real MRI brain images from the Internet Brain Segmentation Repository (IBSR). In addition, a systematic comparison of the proposed algorithm versus five other state of the art techniques is presented. Experimental results show that the proposed algorithm can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provide better results than its competitors