scholarly journals Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yunjie Chen ◽  
Tianming Zhan ◽  
Ji Zhang ◽  
Hongyuan Wang
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Proposed Model ◽  
Brain Mr Images ◽  
The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

scholarly journals Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy CMeans Clustering and EM Algorithm

2011 ◽  
Vol 28 (10) ◽  
pp. 18-26
Author(s):  
Nagesh Vadaparthi ◽  
Srinivas Yarramalle ◽  
Suresh Varma Penumatsa ◽  
Author P.S.R.Murthy
Keyword(s):  
Em Algorithm ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Mr Images ◽  
Brain Mr Images

scholarly journals Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model

2021 ◽  
Vol 11 (11) ◽  
pp. 5213
Author(s):  
Chin-Shiuh Shieh ◽  
Wan-Wei Lin ◽  
Thanh-Tuan Nguyen ◽  
Chi-Hong Chen ◽  
Mong-Fong Horng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Denial Of Service ◽  
Gaussian Mixture ◽  
Mitigation Measures ◽  
Distribution Model ◽  
Ddos Attacks ◽  
Open Set ◽  
The Impact

DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems—the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks’ technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks.

scholarly journals AN ENHANCED METHOD OF LIVER LESION DETECTION USING DEEP NEURAL NETWORK, WATERSHED TRANSFORM AND GAUSSIAN MIXTURE MODEL TECHNIQUES IN MR IMAGES

2021 ◽  
pp. 335-340
Author(s):  
A.BathshebaParimala ◽  
R.S.Shanmugasundaram
Keyword(s):  
Neural Network ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Deep Neural Network ◽  
Gaussian Mixture ◽  
Metastatic Carcinoma ◽  
Neural Network Classifier ◽  
Mr Images ◽  
Malignant Growth ◽  
Aided Diagnosis

Cancer of the liver is one of the leading causes of death all over the world. Physically recognising the malignancy tissue is a difficult and time-consuming task. In the future, a computer-aided diagnosis (CAD) will be used in dynamic movement to determine the precise position for care. As a result, the primary goal of this research is to use a robotized approach to precisely identify liver cancer. Methods: In this paper, we suggest a new approach called the watershed Gaussian based deep learning (WGDL) strategy for accurately portraying malignant growth sores in liver MRI images. This project used a total of 150 images to build the proposed model. The liver was first isolated using a marker-controlled watershed division scale, and the malignancy-induced injury was then divided using the Gaussian mixture model (GMM) algorithm. Different surface highlights were removed from the sectioned locale after tumour division. These jumbled highlights were fed into a deep neural network (DNN) classifier for a computerised classification of three types of liver cancer: haemangioma (HEM), hepatocellular carcinoma (HCC), and metastatic carcinoma (MET). The following are the outcomes: Using a Deep Neural Network classifier and an unimportant approval deficiency of 0.053 during the characterization period, we were able to achieve a grouping precision of 98.38 percent at 150 ages. The system in our proposed approach is suitable for testing with a large data set and can assist radiologists in detecting liver malignant growth using MR images. KEYWORDS: computer-aided diagnosis (CAD), watershed Gaussian based deep learning, Gaussian mixture model, hepatocellular carcinoma, metastatic carcinoma, Deep Neural Network classifier

A Method to Calculate Carbon Footprint of Processing Sectors Based on Gaussian Mixture Model

2013 ◽  
Vol 415 ◽  
pp. 692-696 ◽  
Author(s):  
Xue Ping Liu ◽  
Zhi Shan Liu ◽  
Dong Xiang ◽  
Lang Gao ◽  
Yang Cui ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Carbon Footprint ◽  
Gaussian Mixture ◽  
Statistical Character ◽  
The Impact

Carbon footprint is used to measure the impact of products or services on environment in recently years. The main technology in evaluating carbon footprint comes from LCA and PAS2050. However, the detail in calculating carbon footprint is not well studied. In this paper, the focus was drawn on the processing sectors. Due to the statistical character of data collected in processing sectors, the GMM (Gaussian Mixture Model) is introduced to calculate carbon footprint. With this method, the statistical meaning of carbon footprint data is well understood.

A hybrid model coupling extreme gradient boosting model with Gaussian mixture model for streamflow forecasting

2021 ◽  
Author(s):  
Lingling Ni ◽  
Dong Wang ◽  
Jianfeng Wu ◽  
Yuankun Wang
Keyword(s):  
Water Resources ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Hybrid Model ◽  
Gaussian Mixture ◽  
Gradient Boosting ◽  
Streamflow Forecasting ◽  
Modular Model ◽  
Proposed Model ◽  
Extreme Gradient Boosting

<p>With the increasing water requirements and weather extremes, effective planning and management for water issues has been of great concern over the past decades. Accurate and reliable streamflow forecasting is a critical step for water resources supply and prevention of natural disasters. In this study, we developed a hybrid model (namely GMM-XGBoost), coupling extreme gradient boosting (XGBoost) with Gaussian mixture model (GMM), for monthly streamflow forecasting. The proposed model is based on the principle of modular model, where a complex problem is divided into several simple ones. GMM was applied to cluster streamflow into several groups, using the features selected by a tree-based method. Then, each group was used to fit several single XGBoosts. And the prediction is a weighted average of the single models. Two streamflow datasets were used to evaluate the performance of the proposed model. The prediction accuracy of GMM-XGBoost was compared with that of support vector machine (SVM) and standalone XGBoost. The results indicated that although all three models yielded quite good performance on one-month ahead forecasting with high Nash-Sutclitte efficiency coefficient (NSE) and low root mean squared error (RMSE), GMM-XGBoost provided the best accuracy with significant improvement of forecasting accuracy. It can be inferred from the results that (1) XGBoost is applicable for streamflow forecasting, and in general, performs better than SVM; (2) the cluster analysis-based modular model is helpful in improving accuracy; (3) the proposed GMM-XGBoost model is a superior alternative, which can provide accurate and reliable predictions for optimal water resources management.</p><p>Note: This study has been published in Journal of Hydrology (Ni, L., Wang, D., Wu, J., Wang, Y., Tao, Y., Zhang, J. and Liu, J., 2020. Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model. Journal of Hydrology, 586.).</p>

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