DocumentCode :
665119
Title :
Automatic human brain MRI volumetric analysis technique using EM-algorithm
Author :
Nazari, Mina Rafi ; Singh, Y. Premkumar
Author_Institution :
Fac. of Inf. Technol., Multimedia Univ., Cyberjaya, Malaysia
fYear :
2013
fDate :
21-23 Oct. 2013
Firstpage :
79
Lastpage :
83
Abstract :
The paper presents automated volumetric analysis of human brain MR images for many applications based on the Expectation-maximization (EM) algorithm. It involves voxel labeling, counting, and calculating tissues volume. The voxel labeling requires the brain magnetic resonance image segmentation which is most commonly performed based on voxels intensity signals. A widely used method for segmentation is by creating a Gaussian Mixture Model (GMM) through the EM algorithm and the same can be used to find the tissues, class label and volumes. The experimental results are provided for volumetric analysis of automated segmentation of male and female subjects as well as normal volumes of tissue classes for verifying correctness of automated volumetric analysis and statistical inference for diagnostic applications.
Keywords :
Gaussian processes; biomedical MRI; expectation-maximisation algorithm; image segmentation; medical image processing; mixture models; statistical analysis; EM-algorithm; GMM; Gaussian mixture model; automatic human brain MRI volumetric analysis technique; brain magnetic resonance image segmentation; diagnostic applications; expectation-maximization algorithm; statistical inference; tissues volume calculation; voxel counting; voxel labeling; voxels intensity signal; Algorithm design and analysis; Biomedical imaging; Brain modeling; Image segmentation; Magnetic resonance imaging; Standards; Automatic Volumetric analysis; Brain tissue models; Expectation maximization (EM); Guassian mixture model; Magnetic resonance imaging (MRI);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotic and Sensors Environments (ROSE), 2013 IEEE International Symposium on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4673-2938-5
Type :
conf
DOI :
10.1109/ROSE.2013.6698422
Filename :
6698422
Link To Document :
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