Brain Tissue Classification of MR Images Using Fast Fourier Transform Based Expectation- Maximization Gaussian Mixture Model

2011 ◽  
pp. 387-398 ◽  
Author(s):  
Rajeswari Ramasamy ◽  
P. Anandhakumar
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Brain Tissue ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Gaussian Mixture ◽  
Mr Images ◽  
Tissue Classification

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.

Gaussian Mixture Model of Ground Filtering Based on Hierarchical Curvature Constraints for Airborne Lidar Point Clouds

2021 ◽  
Vol 87 (9) ◽  
pp. 615-630
Author(s):  
Longjie Ye ◽  
Ka Zhang ◽  
Wen Xiao ◽  
Yehua Sheng ◽  
Dong Su ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Latent Variables ◽  
Expectation Maximization Algorithm ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Airborne Lidar ◽  
Height Difference ◽  
Hierarchical Constraints

This paper proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints. Firstly, the thin plate spline function is iteratively applied to interpolate the reference surface. Secondly, gradually changing grid size and curvature threshold are used to construct hierarchical constraints. Finally, an adaptive height difference classifier based on the Gaussian mixture model is proposed. Using the latent variables obtained by the expectation-maximization algorithm, the posterior probability of each point is computed. As a result, ground and objects can be marked separately according to the calculated possibility. 15 data samples provided by the International Society for Photogrammetry and Remote Sensing are used to verify the proposed method, which is also compared with eight classical filtering algorithms. Experimental results demonstrate that the average total errors and average Cohen's kappa coefficient of the proposed method are 6.91% and 80.9%, respectively. In general, it has better performance in areas with terrain discontinuities and bridges.

scholarly journals Detection and Classification of Vehicles on Curved Roads

2019 ◽  
Vol 8 (10S) ◽  
pp. 152-156
Keyword(s):  
Object Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Moving Object Detection ◽  
Morphological Operations ◽  
Moving Vehicles ◽  
Blob Analysis

Detection of a vehicle is a very important aspect for traffic monitoring. It is based on the concept of moving object detection. Classifying the detected object as vehicle and class of vehicle is also having application in various application domains. This paper aims at providing an application of vehicle detection and classification concept to detect vehicles along curved roads in Indian scenarios. The main purpose is to ensure safety in such roads. Gaussian mixture model and blob analysis are the methods applied for the detection of vehicles. Morphological operations are used to eliminate noise. The moving vehicles are detected and the class of the vehicle is identified.

Feature Selection Based on Gaussian Mixture Model Clustering for the Classification of Pulmonary Nodules Based on Computed Tomography

2020 ◽  
Vol 10 (5) ◽  
pp. 1033-1039
Author(s):  
Huihong Duan ◽  
Xu Wang ◽  
Xingyi He ◽  
Yonggang He ◽  
Litao Song ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Feature Selection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Pulmonary Nodules ◽  
Gaussian Mixture ◽  
Local Optimum ◽  
Computer Aided ◽  
Aided Diagnosis

Background: In the pulmonary nodules computer aided diagnosis systems (CAD), feature selection plays an important role in reducing the false positive rate and improving the system accuracy. To solve the problem of feature selection techniques by which the diversity of features was damaged in the process of distinguishing malignant pulmonary nodules from benign pulmonary nodules, this study developed a novel feature selection algorithm for improving the accuracy of traditional computer-aided differential diagnosis for benign and malignant classification of pulmonary nodules. Method: Firstly, we divided the extracted features of nodules into several groups by using Gaussian mixture model (GMM). Secondly, we applied Relief and sequential forward selection (SFS) algorithm to find local optimum features dataset for each group. Afterwards, we used the optimumpath forest (OPF) classifier with the found features dataset to obtain the classification results. Finally, the local optimum features dataset with the highest area under curve AUC in all groups were added into the final selected set. Results: According to collected pulmonary nodules on computed tomography (CT) scans, tested with two set of samples, we achieved an average accuracy of 89.5%, sensitivity of 87.1% and specificity of 90.9% on the first set of samples, and 90.1%, 88.7% and 92.1% on the second set of samples. The areas under the receiver operating characteristic (ROC) curves based on these two sample sets were 95.2%, and 96.3% respectively. Conclusions: This study shows that the proposed method was promising for improving the pulmonary nodules computer aided diagnosis systems performance of benign and malignant pulmonary nodules.

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