scholarly journals Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization

Data in Brief ◽  
2019 ◽  
Vol 27 ◽  
pp. 104628 ◽  
Author(s):  
Ju Qiao ◽  
Xuezhu Cai ◽  
Qian Xiao ◽  
Zhengxi Chen ◽  
Praveen Kulkarni ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Brain Lesion ◽  
Gaussian Mixture ◽  
Model Expectation ◽  
Lesion Segmentation ◽  
Mri Brain

Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization

2020 ◽  
pp. 162-177
Author(s):  
Ichrak Khoulqi ◽  
Najlae Idrissi ◽  
Muhammad Sarfraz
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Ground Truth ◽  
Pectoral Muscle ◽  
Gaussian Mixture ◽  
Model Expectation ◽  
Cad System ◽  
Mammographic Images ◽  
Segmentation Approach

Breast cancer is one of the significant issues in medical sciences today. Specifically, women are suffering most worldwide. Early diagnosis can result to control the growth of the tumor. However, there is a need of high precision of diagnosis for right treatment. This chapter contributes toward an achievement of a computer-aided diagnosis (CAD) system. It deals with mammographic images and enhances their quality. Then, the enhanced images are segmented for pectoral muscle (PM) in the Medio-Lateral-Oblique (MLO) view of the mammographic images. The segmentation approach uses the tool of Gaussian Mixture Model-Expectation Maximization (GMM-EM). A standard database of Mini-MIAS with 322 images has been utilized for the implementation and experimentation of the proposed technique. The metrics of structural similarity measure and DICE coefficient have been utilized to verify the quality of segmentation based on the ground truth. The proposed technique is quite robust and accurate, it supersedes various existing techniques when compared in the same context.

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.

Background based Gaussian mixture model lesion segmentation in PET

2016 ◽  
Vol 43 (5) ◽  
pp. 2662-2675 ◽  
Author(s):  
Chiara Dolores Soffientini ◽  
Elisabetta De Bernardi ◽  
Felicia Zito ◽  
Massimo Castellani ◽  
Giuseppe Baselli
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Lesion Segmentation

scholarly journals Detecting Clusters in Atom Probe Data with Gaussian Mixture Models

2017 ◽  
Vol 23 (2) ◽  
pp. 269-278 ◽  
Author(s):  
Jennifer Zelenty ◽  
Andrew Dahl ◽  
Jonathan Hyde ◽  
George D. W. Smith ◽  
Michael P. Moody
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Clustering Algorithm ◽  
Simulated Data ◽  
Atom Probe ◽  
Gaussian Mixture ◽  
Separation Method ◽  
Detection Accuracy ◽  
Maximum Separation

AbstractAccurately identifying and extracting clusters from atom probe tomography (APT) reconstructions is extremely challenging, yet critical to many applications. Currently, the most prevalent approach to detect clusters is the maximum separation method, a heuristic that relies heavily upon parameters manually chosen by the user. In this work, a new clustering algorithm, Gaussian mixture model Expectation Maximization Algorithm (GEMA), was developed. GEMA utilizes a Gaussian mixture model to probabilistically distinguish clusters from random fluctuations in the matrix. This machine learning approach maximizes the data likelihood via expectation maximization: given atomic positions, the algorithm learns the position, size, and width of each cluster. A key advantage of GEMA is that atoms are probabilistically assigned to clusters, thus reflecting scientifically meaningful uncertainty regarding atoms located near precipitate/matrix interfaces. GEMA outperforms the maximum separation method in cluster detection accuracy when applied to several realistically simulated data sets. Lastly, GEMA was successfully applied to real APT data.

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