3D Method of Using Spatial-Varying Gaussian Mixture and Local Information to Segment MR Brain Volumes

2006 ◽  
pp. 660-671
Author(s):  
Zhigang Peng ◽  
Xiang Cai ◽  
William Wee ◽  
Jing-Huei Lee
Keyword(s):  
Gaussian Mixture ◽  
Local Information ◽  
Brain Volumes ◽  
Mr Brain

scholarly journals A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2391 ◽  
Author(s):  
Hang Ren ◽  
Taotao Hu
Keyword(s):  
Feature Selection ◽  
Objective Function ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Fuzzy Clustering ◽  
Gaussian Mixture ◽  
Local Information ◽  
Segmentation Algorithm ◽  
Membership Degree ◽  
Noise Interference

Since the fuzzy local information C-means (FLICM) segmentation algorithm cannot take into account the impact of different features on clustering segmentation results, a local fuzzy clustering segmentation algorithm based on a feature selection Gaussian mixture model was proposed. First, the constraints of the membership degree on the spatial distance were added to the local information function. Second, the feature saliency was introduced into the objective function. By using the Lagrange multiplier method, the optimal expression of the objective function was solved. Neighborhood weighting information was added to the iteration expression of the classification membership degree to obtain a local feature selection based on feature selection. Each of the improved FLICM algorithm, the fuzzy C-means with spatial constraints (FCM_S) algorithm, and the original FLICM algorithm were then used to cluster and segment the interference images of Gaussian noise, salt-and-pepper noise, multiplicative noise, and mixed noise. The performances of the peak signal-to-noise ratio and error rate of the segmentation results were compared with each other. At the same time, the iteration time and number of iterations used to converge the objective function of the algorithm were compared. In summary, the improved algorithm significantly improved the ability of image noise suppression under strong noise interference, improved the efficiency of operation, facilitated remote sensing image capture under strong noise interference, and promoted the development of a robust anti-noise fuzzy clustering algorithm.

scholarly journals Brain Image Segmentation with Gradient Information

2018 ◽  
Vol 7 (4.38) ◽  
pp. 1392
Author(s):  
Sri Purwani ◽  
Julita Nahar ◽  
Carole Twining
Keyword(s):  
Ground Truth ◽  
Real Data ◽  
Gaussian Mixture ◽  
Previous Method ◽  
Brain Images ◽  
Brain Image ◽  
Gradient Information ◽  
Segmentation Methods ◽  
Mr Brain Images ◽  
Mr Brain

Segmentation is the process of extracting structures within the images. The purpose is to simplify the representation of the image into something meaningful and easier to analyse.  A magnetic resonance (MR) brain image can be represented as three main tissues, e.g. cerebrospinal fluid (CSF), grey matter and white matter. Although various segmentation methods have been developed, such images are generally segmented by modelling the intensity histogram by using a Gaussian Mixture Model (GMM). However, the standard use of 1D histogram sometimes fails to find the mean for Gaussians. We hence solved this by including gradient information in the 2D intensity and intensity gradient histogram. We applied our methods on real data of 2D MR brain images. We then compared the methods with the previous published method of Petrovic et al. on their dataset, as well as on our larger datasets extracted from the same database of 3D MR brain mages, where the ground-truth annotations are available. This shows that our method performs better than the previous method.  

scholarly journals Multilevel Thresholding Method Based on Electromagnetism for Accurate Brain MRI Segmentation to Detect White Matter, Gray Matter, and CSF

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
G. Sandhya ◽  
Giri Babu Kande ◽  
T. Satya Savithri
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Brain Mri ◽  
Ground Truth ◽  
Gaussian Mixture ◽  
Multilevel Thresholding ◽  
Mri Segmentation ◽  
Thresholding Method ◽  
Skull Stripping ◽  
Mr Brain

This work explains an advanced and accurate brain MRI segmentation method. MR brain image segmentation is to know the anatomical structure, to identify the abnormalities, and to detect various tissues which help in treatment planning prior to radiation therapy. This proposed technique is a Multilevel Thresholding (MT) method based on the phenomenon of Electromagnetism and it segments the image into three tissues such as White Matter (WM), Gray Matter (GM), and CSF. The approach incorporates skull stripping and filtering using anisotropic diffusion filter in the preprocessing stage. This thresholding method uses the force of attraction-repulsion between the charged particles to increase the population. It is the combination of Electromagnetism-Like optimization algorithm with the Otsu and Kapur objective functions. The results obtained by using the proposed method are compared with the ground-truth images and have given best values for the measures sensitivity, specificity, and segmentation accuracy. The results using 10 MR brain images proved that the proposed method has accurately segmented the three brain tissues compared to the existing segmentation methods such as K-means, fuzzy C-means, OTSU MT, Particle Swarm Optimization (PSO), Bacterial Foraging Algorithm (BFA), Genetic Algorithm (GA), and Fuzzy Local Gaussian Mixture Model (FLGMM).

scholarly journals Modified Expectation Maximization Method for Automatic Segmentation of MR Brain Images

2013 ◽  
Author(s):  
Meena prakash R ◽  
Shantha Selva Kumari R
Keyword(s):  
Image Segmentation ◽  
Spatial Correlation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Gaussian Mixture ◽  
Brain Images ◽  
Brain Image ◽  
Mr Brain Images ◽  
Mr Brain

An automated method of MR Brain image segmentation is presented. A block based Expectation Maximization method is presented for the tissue classification of MR Brain images. The standard Gaussian Mixture Model is the most widely used method for MR Brain Image Segmentation and Expectation Maximization algorithm is used to estimate the model parameters. The Gaussian Mixture Model considers each pixel as independent and does not take into account the spatial correlation between the neighbouring pixels. Hence the segmentation result obtained using standard GMM is highly sensitive to Inensity Non-Uniformity and noise. The image is divided into blocks before applying EM since the GMM is preserved in the local image blocks. Also, Nonsubsampled Contourlet Transform is employed to incorporate the spatial correlation among the neighbouring pixels. The method is applied to the 12 MR Brain volumes of MRBRAINS13 test data and the White Matter, Gray Matter and CSF structures were segmented.

scholarly journals P2-210 Regional MR brain volumes in the prediction of cognitive decline in ad

2004 ◽  
Vol 25 ◽  
pp. S290
Author(s):  
Wes S. Houston ◽  
Christine Fennema-Notestine ◽  
Nikki R. Horne ◽  
Mark W. Bondi ◽  
Sarah L. Archibald ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Brain Volumes ◽  
Mr Brain
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